Research

Ongoing research:

Previous research:

 

 

IEEE 802.16 (WiMAX) Systems

QoS Mechanism for the Mesh Operating Mode of IEEE 802.16

While there is a well-defined QoS mechanism in the PMP operating mode of IEEE 802.16. However, the same cannot be said for the Mesh operating mode. In order for the Mesh operating mode of IEEE 802.16 to be a promising and effective broadband wireless access technology it must include clearly structured QoS handling mechanism. Addressing this issue, we have developed a QoS mechanism for the Mesh operating mode of the IEEE 802.16 standard. Similar to the QoS mechanism that is developed for the PMP mode, our mechanism defines four different QoS levels by which all downlink and uplink traffic are mapped. Using our approach traffics that have higher importance in respect to delay, jitter, and dropped packet rate greatly benefits compared to the standard method of the Mesh operating mode of IEEE 802.16.

Figure 1. Measurement of 0-6 GHz spectrum utilization at Berkeley Wireless Research Center

 

Cross-Layer Scheduling-Routing in the Mesh Mode of IEEE 802.16

In the Mesh operating mode of IEEE 802.16, both the Internet and Intranet traffic share the same network capacity. Being a broadband access technology the importance and load of the Internet traffic are greater than the Intranet traffic in a typical WiMax cell. The network capacity can only be statically allocated between these two traffics. We have developed a cross layer routing-scheduling scheme to circumvent this allocation limitation of the mesh mode of IEEE 802.16. Thus, the bandwidth allocated to Intranet traffic can be used for the Internet traffic in cases of high Internet traffic loads.

 

Increasing the Speed of the Bandwidth Allocation Mechanism of the Intranet Traffic in the Mesh Mode of IEEE 802.16

The IEEE 802.16 standard uses a scheduling method called, the distributed scheduling, for the management of the Intranet traffic in the network in the Mesh operating mode. The performance of the Intranet traffic is greatly based on the speed of this management mechanism since its most important task is allocation of the nodes to the wireless medium. We have shown that the current bandwidth allocation mechanism can be hastened without any significant change to the standard. In this work our aim is to increase the speed of the bandwidth allocation mechanism of the Intranet traffic with an advanced method that fully complies with the IEEE 802.16 standard.

 

Reducing the MAC Overhead in the PMP Operating Mode

The continuous transmission of a IEEE 802.16 network is divided into some fixed duration parts called frames that consists of two subframes; downlink and uplink. The usage of these subframes is determined by the cell’s Base Station(BS). This information is broadcasted by BS through some maps called, the downlink map (DL-MAP) and uplink map message (UL-MAP). These maps are sent to the user stations at the beginning of each frame. The size of the UL-MAP affects capacity for the transmission of data. We propose a new algorithm to decrease size of the UL-MAP control message and thus increase the amout of frame allocated to actual data transfer.

 

Performance Analysis of the Mobility Capability of IEEE 802.16e

A relatively new member of the IEEE 802.16 standard family, IEEE 802.16e (also known as Mobile WiMax) brings mobile user support to WiMax. Currently a standard at its infancy, it has to support various mobility qualities in order to be a competitive 3G wireless network standard. The most important qualities of this new standard is the ability of the user’s seamless handover between neighboring WiMax cells and the battery life of a typical Mobile WiMax device. In the standard, three different handover types are defined, with different system requirements and performances. Our work is focused on the handover capability of the Mobile WiMax. First we evaluate the performance of the current methods and then develop methods to increase the Mobile WiMax’s mobility capabilities.

Our publication(s) related with this project:

 

Ç Top of page

Cognitive Radio

(Dynamic Spectrum Access Networks)

Cognitive Radio

It is commonly accepted that the wireless spectrum is the scarce resource in wireless communications. However, more than 95% of the usage is below 3GHz. The static assignment of the spectrum results in inefficient usage of the spectrum. The utilization of the spectrum can be as low as 10% [r1]. Dynamic access to the spectrum can be considered as a method for solving spectrum scarcity. The Federal Communications Commission (FCC) in the U.S.A. is reviewing its policies regarding the usage of licensed bands by unlicensed users [r2]. (The use of the spectrum in Turkey is regulated by Telekomunikasyon Kurumu. Information about the spectrum use in Turkey may be retrieved at http://www.tk.gov.tr/Duzenlemeler/teknik/marfl/_marfl1.asp)

Figure 2. Measurement of 0-6 GHz spectrum utilization at Berkeley Wireless Research Center [r3]

Cognitive Radio (CR) is a paradigm for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. This alteration of parameters is based on the active monitoring of several factors in the external and internal radio environment, such as radio frequency spectrum, user behavior, and network state. The idea of CR was first proposed by Joseph Mitola III and Gerald Q. Maguire, Jr in [r4]. It was thought of as an ideal goal towards which a Software-Defined Radio (SDR) platform should evolve: a fully reconfigurable wireless black-box that automatically changes its communication variables in response to network and user demands.

Definitions

Mitola's definition: A radio driven by a large store of a priori knowledge, searching out by reasoning ways to deliver the services the user wants [r4].

Haykin's definition: Radios that improve spectral efficiency by sensing the environment and then filling the discovered gaps of unused licensed spectrum with their own transmissions [r5].

Consciousness: This term refers to awareness of one's own existence, sensations, thoughts, surroundings, etc., including emotions and free will.

Cognitiveness: This term refers to mental processes of perception (sensing), memory, judgment, and reasoning.

 

This project is focused on Cognitive Radio Networks (CRN). My research addresses the following issues in CRN:

 

CR Physical Platform

Cognitive radio [r4] is considered as the key enabling technology of Next Generation Wireless Systems (NGWS) and dynamic spectrum access systems. Cognitive Radio Mobile Terminal (CogMT) is defined as an intelligent wireless communication device that works on Software Defined Radio (SDR) physical platform [r6]. It has the capabilities of knowing and understanding the spectral environment it is operating in, adapting its physical radio parameters to the changing spectral conditions and learning from its radio environment [r5]. An SDR system is a radio communication system that can potentially tune to different frequency bands and receive any modulation across a large frequency spectrum by means of as little hardware as possible, processing the digitized signals in software. In the context of NGWS, cognitive radio enables mobile users to dynamically access and to fairly share the spectrum with other users while benefiting from the advantages of available subsystems in NGWS. In the literature the cognitive radio users are called Secondary Users (SUs) and the licensed users are called Primary Users (PUs) of those bands [r7]. SUs access the spectrum dynamically for underutilized bands without causing harm to PUs, as a result, spectrum utilization increases. The cognitive radio is, therefore, an accelerating trend of the current wireless technology research.

The ideal receiver scheme would be to attach an analog to digital converter to an antenna. A digital signal processor would read the converter, and then its software would transform the stream of data from the converter to any other form the application requires. An ideal transmitter would be similar. A digital signal processor would generate a stream of numbers. These would be sent to a digital to analog converter connected to a radio antenna. The ideal scheme is, due to the actual technology progress limits, not completely realizable, however. The actual practical solution is to let the software processing stage be preceded by a front-end that preconditions the input signals to give them characteristics that enable the subsequent stage to elaborate them. Current (2007-2008) digital electronics are not sufficient to receive directly typical radio signals over approximately 40MHz. An ideal software radio has to collect and process samples at more than twice the maximum frequency at which it is to operate. Actual software radios, for frequencies below 40MHz, use a direct-conversion hardware solution. In this solution an analog-to-digital converter (ADC) is connected almost directly to the antenna (some preamplifier and impedance adapting circuitry is present to ensure that the input of the ADC is correctly matched to the antenna). The output stream of digital data obtained from the ADC is then passed to the software defined processing stages.

Our focus on this subject is the software architecture design of handheld devices that works on SDR physical platform. CogMT device design also includes the artificial intelligence capabilities. Regardless of operating frequency range, a wideband front-end for a cognitive radio could have an architecture as depicted in Figure 3. The wideband RF signal presented at the antenna of a cognitive radio includes signals from close and widely separated transmitters and from transmitters operating at widely different power levels and channel bandwidths. As a result, detection of weak signals must frequently be performed in the presence of very strong signals. Thus, there will be extremely stringent requirements placed on the linearity of the RF analog circuits as well as their ability to operate over wide bandwidths. In order to keep the requirements on the final analog to digital (A/D) converter at a reasonable level in a mostly digital architecture, front-end design needs a tunable notch analog processing block that would provide a dynamic range control.

Figure 3. Physical architecture of CR: (a) CR transceiver and (b) wideband RF/analog front-end architecture [r7].

RF hardware for the cognitive radio should be capable of tuning to any part of a large range of frequency spectrum. Also such spectrum sensing enables real-time measurements of spectrum information from radio environment. Generally, a wideband front-end architecture for the cognitive radio has the following structure as shown in Figure 3(b). The components of a cognitive radio RF front-end are presented in [r7]:

RF filter: The RF filter selects the desired band by bandpass filtering the received RF signal.

Low noise amplifier (LNA): The LNA amplifies the desired signal while simultaneously minimizing noise component.

Mixer: In the mixer, the received signal is mixed with locally generated RF frequency and converted to the baseband or the intermediate frequency (IF).

Voltage-controlled oscillator (VCO): The VCO generates a signal at a specific frequency for a given voltage to mix with the incoming signal. This procedure converts the incoming signal to baseband or an intermediate frequency.

Phase locked loop (PLL): The PLL ensures that a signal is locked on a specific frequency and can also be used to generate precise frequencies with fine resolution.

Channel selection filter: The channel selection filter is used to select the desired channel and to reject the adjacent channels. There are two types of channel selection filters. The direct conversion receiver uses a low-pass filter for the channel selection. On the other hand, the superheterodyne receiver adopts a bandpass filter.

Automatic gain control (AGC): The AGC maintains the gain or output power level of an amplifier constant over a wide range of input signal levels.

In this architecture, a wideband signal is received through the RF front-end, sampled by the high speed analog-to-digital (A/D) converter, and measurements are performed for the detection of the licensed user signal. However, there exist some limitations on developing the cognitive radio front-end. The wideband RF antenna receives signals from various transmitters operating at different power levels, bandwidths, and locations. As a result, the RF front-end should have the capability to detect a weak signal in a large dynamic range.

 

CRN Architecture

According to FCC, several parts of the fixed spectrum are under-utilized while some spectrum bands are heavily used and subject to high interference [r8]. Temporarily unused spectrum bands (a.k.a. spectrum holes or white spaces) can be used by opportunistic radios to improve the overall spectrum utilization. Hence, new spectrum allocation methods and technologies are necessary to maximize the benefits of the limited spectrum resource by learning the unused spectrum bands in given time and location. Dynamic Spectrum Access (DSA) technique aims to solve spectrum allocation problems. The overall system that learns the operating environment and adapts its operating parameters according to its surrounding and uses DSA technique for efficient spectrum usage is called Cognitive Radio Network (CRN). In Figure 4, spectrum holes are employed by CRN in an example scenario. In different areas the usage of the spectrum differs, so spectrum hole locations and their durations vary. CRN uses these spectrum holes for providing service to its users without causing harm to other users. Therefore, the change of parameters are observed by active monitoring of several factors in the external and internal radio environment, such as radio frequency spectrum, user behavior and network state.

Figure 4. CRN frequency usage example

In the architectural foundation, we define some network agents to manage and support DSA nature of cognitive networks. The following terms are necessary for our proposed architecture:

Frequency Holder (FH): FH represents the institution that has the right of using a spectrum band in a particular region by a long term leasing agreement with the governmental agencies.

Spectrum Broker (SB): SB is a network agent that interconnects wireless spectrum holder and the CR users.

Cognitive Radio Service Provider (CRSP): CRSP is an entity that provides cognitive radio services.

Cognitive Radio Service Provider Network (CRSPN): CRSPN is a cellular network which covers a broad geographical area and provides communication service for cognitive radios. A CRSPN can be owned by a CRSP or may be shared between some CRSPs. Moreover, establishment of these networks by some third party institutions is also possible. These institutions provide leasing of their networks by CRSPs.

Cognitive Radio Mobile Terminal (CogMT): Defined as an intelligent wireless communication device that works on Software Defined Radio (SDR) physical platform [r6].

Cognitive Base Station (CogBS): CogBS works as a connection point that ties CogMTs to CRSPN and responsible for handling traffic and signaling between a CogMT and the CRN.

 

Advantages of Establishing CRSPN:

Disadvantages of Establishing CRSPN:

 

We propose a network architecture for CRN that establishes CRSPN [p1]. Nodes in CRN has capabilities of transmitting and receiving at different frequency bands. CogMTs use frequencies as long as they don’t cause harm to primary users of the frequencies. Broker systems are introduced in the network architecture for learning and leasing the frequency holes in the channel. Entities and the interconnections of them are summarized in the Figure 5.

Figure 5. Architecture of CRN

We focus on architectural design of CRN and define the messages within interactions between entities. Our current research on this subject is presented on [p2] with flow charts of the interactions and the architectural design of the CRN.

Software Defined Radio

A Software Defined Radio (SDR) is a radio communication system which can tune to any frequency band and receive any modulation across a large frequency spectrum by using the same hardware as possible and process the signals through software. From mobile communication perspective it means user access to multiple systems from a single terminal. The logic behind a software radio is based on replacing the radio hardware components (Analog to digital converters, digital to analog converters, modulators, filters…) with the equivalent piece of software. In order to handle performance issues related to fast signal processing, FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) based processing hardware is used instead of GPP (General Purpose Processor).

 

Figure 6. SDR infrastructure

The opportunities that the SDR offer makes it a candidate platform to solve the problems related to dynamic spectrum management. Capabilities of an SDR allow the handset to operate in heterogeneous wireless networks. In other words, an ideal SDR handset can dynamically change its running software and can tune to required frequency and modulation type at runtime.

Software Defined Radio As a Base of Cognitive Radio

Here are the definitions of both Cognitive Radio and SDR.

 When we map these two definitions to each other, it is noticed that SDR can satisfy the required flexibility that Cognitive Radio needs.

 

Figure 7. Relationship of SDR and Cognitive Radio.

Our publication(s) related with this project:

[p1] D. Isler, H. B. Yilmaz, A. Zumbul, and T. Tugcu, "Akýllý Radyo Aðlarý Ýçin Bütünsel Bir Mimari," submitted to SIU 2008.

[p2] D. Isler, H. B. Yilmaz, A. Zumbul, and T. Tugcu, "A Holistic Architecture for Cognitive Radio Networks," in preparation.

References

[r1] N. Devroye, P. Mitran, et al., "Limits on communications in a cognitive radio channel," IEEE Communications Magazine, Vol.44, No.6, pp. 44-49, 2006.

[r2] FCC, “Spectrum policy task force report,” ET Docket No.02-155, Nov 2002.

[r3] D. Cabric, S. M. Mishra, et al., "Implementation issues in spectrum sensing for cognitive radios," 38th Asilomar Conference on Signals, Systems and Computers, 2004.

[r4] J. Mitola III and G. Q. Maguire, Jr., "Cognitive radio: making software radios more personal," IEEE Wireless Communications, Vol. 6, No. 4, pp. 13-18, 1999.

[r5] S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE Journal on Selected Areas in Communications, Vol. 23, No. 2, pp. 201-220, 2005.

[r6] K. J. Friedrich, "Software-defined radio: basics and evolution to cognitive radio," EURASIP Journal on Wireless Communication Networks, Vol. 5, No. 3, pp. 275-283, 2005.

[r7] I. F. Akyildiz, W.-Y. Lee, et al., "NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey," Computer Networks, Vol. 50, No. 13, pp. 2127-2159, 2006.

[r8] P. Kolodzyetal, “Spectrum Policy Task Force Report,” Technical report, 2002.

 

Ç Top of page

Next Generation Wireless Systems (NGWS)

The Next Generation Wireless Systems project is funded by TUBITAK. The project addresses the following issues in NGWS:

To evaluate these new methods, we designed a testbed for NGWS using only WLAN access points.

NGWS Architecture

(NGWS) will provide high bandwidth access anytime, anywhere for services including multimedia with QoS requirements. Existing systems fail to satisfy all NGWS objectives simultaneously due to constraints like global coverage, indoor/outdoor communications, and frequent handoffs. Therefore, NGWS will combine the existing technologies and the new technologies to come to provide high bandwidth access everywhere. PCS, WLAN, satellite, and new wireless systems like 4G Mobile will serve as subsystems in NGWS. The basic properties of NGWS can be summarized as follows:

The architecture of NGWS is shown in the following figure.

Figure 8. NGWS network architecture

NGWS Testbed

Designing a testbed for NGWS is a significant challenge due to the presence of several subsystems with different air interfaces. Besides the high cost of the purchase, deployment, configuration, and maintenance of  all the equipment, the licenses required for the frequencies constitute the major challenges.

We propose a testbed for NGWS using only WLAN access points. The proposed testbed emulates several subsystems simultaneously at the network layer, allowing network and higher level research. Since WLAN systems use ISM bands, frequency licensing does not constitute a problem.

Figure 9. NGWS testbed

NGLR: Next Generation Location Registration

Overlapping coverage areas of the systems in next generation networks cause high signaling overhead if the users are tracked in multiple systems independently. Selecting the system over which paging will be done is yet another problem. We propose a location registration scheme that updates the location information only in the relevant subsystems. We also propose an efficient paging scheme that exploits the location information in multiple subsystems. User preferences, network availability, and connection history are considered while determining the subsystems to be used for location registration and paging.

Since a Mobile Terminal (MT) has access to multiple subsystems in NGWS, it can cross more registration area boundaries with respect to the single subsystem case. Therefore, MT must send more location registration messages. More location registration messages implies more signaling overhead in the system, and more power consumption at the mobile terminal. With NGLR, we propose sending location registration messages selectively only in the subsystems that are relevant for the user. The location registration messages for the non-relevant subsystems are suppressed. Thus, both the signaling cost and the power consumption are reduced at the cost of reduced precision of the location information in the subsystems that are non-relevant.

NGP: Next Generation Paging

The network’s knowledge about the location of an inactive user is in registration area level. When an incoming connection request for a mobile user is received, the network has to locate the user in the cell level to set up the connection. In order to locate the user, the network broadcasts a polling message in all cells in the user’s last known registration area. This widely used scheme is known as blanket paging.

In NGWS, paging over all subsystems is not feasible since it implies that all mobile terminals are paged over all subsystems. Paging signaling cost can be reduced by choosing only one subsystem to broadcast paging messages. It is possible to reduce even further by leveraging the existence of multiple subsystems in the same service area. With NGP, we propose to broadcast paging messages in a smaller number of cells subject to paging delay constraints.

NGCAC: Next Generation Connection Admission Control and Handoff

In a wireless network, it is connection admission control scheme that decides whether connection and handoff requests will be accepted. For new connection requests, the NGWS must select the appropriate subsystem for connection establishment. Furthermore, the mobility of a user may require change in the use of wireless resources, resulting in a handoff attempt for the user. It is the duty of NGCAC to manage the connection requests and handoff attempts in a way that maximizes network utilization, minimizes outage, and distributes the load between subsystems.

Since MT has access to multiple subsystems simultaneously, the NGWS must select one of the subsystems for connection. Among the accessible subsystems, one subsystem that can accommodate the connection request will be selected subject to connection class and user preferences.

In the analysis of wireless systems, the service area is typically split into cells since the partitioning criteria is the access node that controls the area. However, in the case of NGWS, cellular granularity is too coarse to define a partition since there are multiple subsystems serving the same service area. Therefore, in our model, the service area is partitioned into smaller regions we call physical areas. This is the first analytical model for NGWS in the literature.

We consider the following elementary events in our mathematical model.

Our publication(s) related with this project:

 

Ç Top of page

Mobility Modeling for Vehicular Ad Hoc Networks (VANET)

(This project was developed in collaboration with Dr. Cem Ersoy.)

VANET is a special research area in Mobile Ad-Hoc Networks (MANETS) dealing with wireless inter-vehicle communication between traveling vehicles and roadside units without the help of any fixed infrastructure. Data may consist of a variety of things such as sensor information from other cars. Vehicles can exchange information about real-time traffic congestion, road condition and maintenance, car accidents and regional weather forecast. Different safety applications can be developed using VANETS such as crash avoidance, automated highway systems, trip planning, and driver assistance.

The validity and performance of proposed mechanisms are evaluated by mobility models, which, in this case consider vehicle mobility behavior. As a result, the accuracy of the model is important for the evaluation of different network design alternatives. A realistic modeling of mobility helps achieving better performance in real deployment, so a simulation tool that is able to capture some features of vehicle mobility becomes an important part of this process.

In this work, a new vehicular mobility model is introduced, as well as a simulation tool for more realistic and accurate evaluation of performance of VANETS.

Objectives:

After the initial distribution of the nodes, mobility model determines the location of nodes in the topology at any given instant, constructing movement paths of nodes within the network. In contrast to mobile ad-hoc networks where node movement occurs in an open field, vehicular movements in VANETS are restricted by different factors as stated above.

Layout of Streets: Streets defined by map data for real cities constrains node movement pattern. For example a car traveling on a road is likely to follow the path of the road. Nodes confine their movements to well defined paths separated by buildings, trees or other obstructions, exhibiting some degree of regularity in mobility patterns. This determines the average distance between nodes, distribution of nodes node and connectivity of the network.

Traffic Control Mechanisms: Real world artifacts specific to urban settings must be considered such as stop signs and queuing of vehicles at intersections which result in formation of clusters and limits mobility.

Interdependent Vehicular Motion: The movement of a vehicle is guided by the movement of other vehicles surrounding it according to the minimum distance from the vehicle in front of it, increase or decrease in its speed, etc.

Speed Limit: The speed limit on a road determines the rate in the change of a vehicle’s position, which in turn determines the network topology changes.

Block Size: A city block can be considered as the smallest area containing several buildings and surrounded by streets, which determines the number of intersections in the area.

A mobility modeling tool is proposed, which considers the basic mobility features of vehicles stated above, for better evaluation of VANETs. The mobility model is integrated into OPNET.

Our publication(s) related with this project:

 

Ç Top of page

An End-to-End QoS Architecture for All-IP Networks

IEEE 802.20 [4], Mobile Broadband Wireless Access (MBWA) Working Group, has been established with the mission of developing the specification for an efficient packet based air interface that is optimized for the transport of IP based services. In all-IP 4G networks, Internet Protocol (IP) packets are expected to traverse an access network and backbone network without any protocol conversion [5], hence 802.20 is a good candidate as air interface of converged all-IP 4G networks. Among the proposals to 802.20, Flash-OFDM (Fast Low-latency Access with Seamless Handoff OFDM) [6], that is an air interface technology designed for the delivery of advanced Internet services in the mobile environment, is promising. Flash-OFDM is a system based on OFDM, and also specifies higher protocol laters. It extends packet switched domain to air interface to fully implement the all-IP idea. A challenging issue in such a network is providing strict Service Level Agreements (SLA) with strict QoS requirements.

In this project, an end-to-end QoS architecture for all-IP 4G networks is proposed. The architecture includes a new Medium Access Control Layer (MAC) for Frequency Hopping Orthogonal Frequency Division Multiplexing (FH-OFDM) air interface, Multi Protocol Label Switching (MPLS) based core network, and integrated QoS for air interface and core network based on Differentiated Services (DiffServ). Multi Protocol Label Switching (MPLS) and DiffServ support of MPLS is utilized at the backbone of the architecture to provide end-to-end QoS. By integrating IP Layer QoS parameters to proposed packet-based air interface, end-to-end QoS including the air interface is provided.  Two new nodes, Wireless Access Router (WAR) and Mobile Node (MN), which implement the new MAC layer are introduced. WAR is integrated to the backbone via standard Label Edge Router (LER) of MPLS architecture. The architecture is implemented with OPNET Modeler simulation tool to analyze the QoS perception of MNs within the architecture.

Figure 10. Extending QoS to IEEE 802.20 MBWA

Our publication(s) related with this project:

 

Ç Top of page

Routing in Wireless Sensor Networks for Disaster Monitoring

With increasing functionality and decreasing costs, today’s wireless sensor networks (WSNs) are heavily used for services such as surveillance, target positioning, and habitat control. In a WSN, usually a large number of sensors are spread over the area of interest. After deployment, these low-cost, low-power sensors monitor the area and communicate with sink node(s) either directly or by use of other sensors as relays.

The most important use of WSNs is for surveillance and monitoring. Due to limited energy of the whole network, careful strategies should be used for saving energy to increase network lifetime. These strategies include, but are not limited to, careful deployment of sensors, in-network processing of data, data aggregation, clever routing techniques. Most of these routing techniques employ clustering schemes for efficient data transmission and aggregation.

Careful deployment of sensors is virtually impossible since sensors are usually dropped from an aircraft. Even when they are deployed by hand, a fine resolution is difficult to achieve due to environmental factors like wind, terrain conditions, etc.

In the literature, significant amount of work has been published on the issue of clustering. However, these approaches perform clustering according to the location and communication ranges of the sensors. Therefore, the clusters are created according to the network criteria rather than the environment. In our approach, we perform clustering according to the location of the events being sensed and sensing ranges of the sensors. Thus, the clusters are formed around the events under consideration.

Scenario and Objectives:

In this work, we assume that WSN is deployed either before disaster or soon after the disaster strikes. If it is deployed before disaster, it operates regularly until the occurrence of the disaster. The disaster may occur at any part of the coverage area; moreover different instances can coexist at the same time. We are interested in disasters that may causes multiple events in the area and expand, such as fire or radioactive leakage. An example scenario is shown in Figure 11 where red objects represent events and nodes inside the blue regions contain the sensor nodes which detect the events.

Figure 11. A possible scenario of multiple fires

 Our objective in this work is to design a routing protocol for the network after the occurrence of disaster. We assume that sensor nodes near the events always sense the medium to observe the changes in the environment. The remaining sensors relay the data packets from these nodes and periodically sense the medium (for temperature, radiation level, etc.) and also observe whether the event is expanding or not. The sensors nearby the disaster area form a cluster for routing and to make data aggregation as necessary. Since the events may expand and destroy nearby sensors, new nodes may be added to the cluster or some nodes may become nonfunctional. These events may result in the expansion, shrinkage of a cluster. It is also possible that a cluster may be split into two clusters. In our work, we will also find when a node should be added to a cluster, when a cluster should be split into two, and which node should be the cluster head. We will also consider energy conservation in the design of our routing scheme to extend network lifetime.

Our publication(s) related with this project:

 

Ç Top of page

An Efficient Routing Algorithm for Heterogeneous Wireless Sensor Networks

Wireless Sensor Network (WSN) consists of a number of transducers with wireless communication ability intended to monitor the conditions at specific areas. Each sensor node is equipped with a microcontroller, a small memory unit, an energy source, and a communication component. Advances in sensor technology and decreasing costs increase the interest in he application of WSNs to various areas such as battlefield surveillance, smart houses, traffic control, habitat monitoring, and industrial automation.

The data collected by the sensors are generally routed to a sink node for proper action. The routing protocols proposed in the literature can be roughly categorized in two groups: Geographic and Non-geographic. Due to scalability and energy efficiency, geographic routing has received more attention.

One of the fundamental requirements of WSNs is full coverage of the environment it is deployed to sense. Since the environment is generally hostile, it is difficult to deploy the network as desired. Generally the deployment of sensors is done from the air resulting in random loci for the sensors. As a consequence of this random deployment, the paths from the sensing area to the sink(s) concentrate over a small subset of sensors, called bottleneck sensors. Our aim in this project is to introduce a limited number of more capable sensors to the field to overcome the problem of bottleneck sensors. We will also develop QoS-aware routing algorithms to provide different service levels in routing in terms of end-to-end delay and reliability.

Most of the related work in the literature aims finding out how many sensors should be deployed over the target to make a reasonable coverage. Deploying mobile sensors is another solution to the coverage problem. However, such approaches increase the cost and computational load significantly. In our approach, we will devise an algorithm to estimate the ratio of regular and special sensors.

Scenario and Objectives:

In this work, we assume that deployment area is unknown or hostile. After deployment, parts of the target environment may not be monitored as a result of geographical shapes, obstacles, damaged nodes. As shown in the figure below, a river splits the network into two subnetworks, and one of the subnetworks is not able to reach the sink to transmit the sensed data. Also, an obstacle (e.g. a rock) may cause the sensors to be spread out irregularly. As opposed to the case of the river, the data flow may still be carried out around the obstacle, but in many hops. For instance, a message from node A will need to turn around the obstacle to reach node C. The data flow going from the north of the obstacle will have to transmit over node B, exhausting its battery quickly, resulting in yet another split in the network.

 

Figure 12. A possible scenario where sensor distribution is severely affected by the terrain

Our objective is to develop a more reliable and delay-aware design, increasing network life time, and decreasing hop count. We assume that in order to deploy more sensors to increase network life time, we can use special, more powerful sensors mixed with regular sensors. This approach also solves the problems of network splits and bottlenecks. As shown in the figure below, we deploy some powerful sensors without increasing the cost of the network. The range and the battery life of these special sensors are better than the regular sensors. These special sensors are used as bridge sensors to connect two sides of network. Also, the special sensor nodes around the obstacle decrease the hop count.

Figure 13. A possible scenario with mixed type sensors

Having long range sensor nodes also gives us the opportunity to generate alternative and faster routes. In case of different classes of data, the faster routes may be used to carry urgent data to the sink. As a conclusion we will try to create a more reliable network and use the advantages of special nodes to create alternative and faster routes. For this purposes, we will work on a routing algorithm for carrying mixed classes of data over mixed type of nodes to have a long network life time.

Our publication(s) related with this project:

 

Ç Top of page

Fingerprinting and A-GPS Approaches for Location Estimation in Cellular Networks

The popularity of Location Based Services (LBS) is increasing day by day. LBS are offered by cellular phone networks as a way to send custom advertising, tracking, and navigation. One example is finding the nearest business of a certain type. A cell phone user may wish to find the nearest taxi by querying the operator. Another application is tracking the vehicles of a cargo company.

The service provider is able to get the location of the cellular phone in different ways. One of the solutions is to determine location via a GPS chip built into the phone. Another approach is to use radiolocation and trilateration based on the signal strength of the Base Transceiver Stations (BTS).

The current technology has approximately 300 meters accuracy. There is ongoing research to increase accuracy and the usability of the LBS services. Assisted GPS (A-GPS) is a technology that uses an assistance server to cut down the time needed to determine a location using GPS. Also, using GPS devices are very energy consuming for cell phones so it is not efficiently applicable. A-GPS is useful in urban areas. If the user is located in near the tall buildings, under heavy tree cover and in indoors, A-GPS is more suitable for finding the location of user than GPS. A-GPS is different from the regular GPS. It adds another element to the equation which is the Assistance Server. In regular GPS networks, there are only GPS satellites and GPS receivers. In A-GPS networks, the receiver cell phones are limited in processing power and generally in locations less suitable for position fixing. Cell phones communicate with the assistance server that has high processing power and access to a reference network. In this sceanario, both A-GPS receiver and the Assistance Server share tasks.

Fingerprinting is another research area to increase the accuracy of LBS. In this scenario, data are collected from the network to form a location database. After sufficient signal strength and location pairs have been collected, this fingerprint may be utilized to determine the location of the cellular phone with a certain accuracy.

In this project, we will study both A-GPS or Fingerprinting approaches for inreasing the accuracy of LBS and decreasing both processing power and the time that passes through the calculation process.

Our publication(s) related with this project:

 

Ç Top of page

Environment Aware Location Estimation in Cellular Networks

(The Environment Aware Location Estimation in Cellular Networks was supported by Vodafone IT. It was developed in collaboration with Dr. Fatih Alagöz.)

Location Based Services (LBS) enable personalized services to the mobile subscribers based on their current position. They provide new opportunities for cellular operators as well as application and content providers for the provision of innovative value added services and creation of new revenue sources. Consequently, mobile positioning in wireless systems has received significant attention in both research and industry over the past few years since it plays a key role in providing location based services such as location-based billing, intelligent transportation systems, and the wireless emergency services.

Mobile positioning involves a variety of technologies, which are divided into two major categories: network-based and handset-based location estimation. Handset-based positioning methods require a modified handset to calculate its own position, for instance by using a Global Positioning System (GPS) receiver embedded in the handset. The drawbacks of using handset-based methods are the cost of deploying new handsets, delay for the adoption of LBS due to slow spread of these new handsets and cost of developing a suitable low-power and economical integrated technology for the wireless communication systems. On the contrary, using network-based methods for mobile positioning in wireless communication systems has its advantages. When compared to the handset-based methods, the network-based methods are relatively less complex. However, although they can be used in many situations where GPS-based methods cannot be applied (such as indoor positioning), the mobile positioning in the network-based methods is generally less accurate than that in handset-based methods. Thus, the improvement of the accuracy for mobile positioning becomes an important issue especially for network-based solutions.

In this project, we introduce a machine learning based environment-aware location estimation method, namely Environment Aware RSS Based Location Estimation (EARBALE), and evaluate its performance. We utilize signal measurements in a 900 MHz GSM network and data compiled from Istanbul, the largest city in Turkey with more than 10 million residents. EARBALE method uses preprocessing and dimensionality reduction via decision tree (DT) on these bulk empirical data and apply artificial neural network (ANN) based classification for the adoption of the most appropriate wireless channel model. By this way, our trained ANN is capable of identifying the environment of an input measurement as either urban, suburban, or rural. An urban area is an area with an increased density of human-created structures in comparison to the areas surrounding it. A suburban area is an inhabited district located either inside a town or city's limits or just outside of it. A rural area is a sparsely settled place away from the influence of large cities. According to the identification of the most probable environment, the localization algorithm uses the corresponding Hata propagation model in the triangulation phase. We evaluate and compare EARBALE to the generic RSS based localization method that uses triangulation with Hata's default urban propagation model. By this way, we investigate the importance of environment estimation for any RSS based localization algorithm.

Figure 14. EARBALE scheme

Our publication(s) related with this project:

 

Ç Top of page

Mobility-Based Connection Admission Control (MBCAC)

This project was part of my PhD thesis under the supervision of Dr. Cem Ersoy.

The mobility of the subscribers constitutes the major challenge in cellular networks . A subscriber who is moving during conversation may end up in a cell where the wireless resources are not sufficient to handle the call. The forced termination of the call against the will of the subscriber, due to lack of wireless resources, is called call dropping (or forced call termination). Call dropping is different from the rejection of a new call request, call blocking, and is considered more annoying.

It is a common approach to reserve some of the nominal channels, called guard channels, for possible handoff calls in order to reduce the call dropping rate. When guard channels are used, a new call request is admitted only if there is a free nominal channel. On the other hand, a handoff request is admitted as long as there is a free nominal or guard channel. Thus, handoff calls are privileged over new call requests. The number of guard channels must be large enough to prefer handoff calls to new call requests, and small enough to avoid waste of resources by blocking calls unnecessarily. However, calculation of the optimal value is impractical since subscriber density is time- and space-dependent.

We propose the Mobility-based Call Admission Control (MBCAC) scheme that estimates the number of guard channels for each cell according to the mobility patterns of the individual subscribers so that the call dropping rate is decreased at a lower cost. The controlling base station creates an elliptical reservation area on behalf of each conversing mobile station. The size, shape, and the orientation of the reservation area are defined by the mobility pattern of the subscriber. A likeliness value, which shows how likely the subscriber will visit the cell in question, is calculated for each reservation request. Due to the statistical accumulation of the reservation requests, the MBCAC scheme enjoys a smaller increase in the blocking rate.

The flowchart of MBCAC scheme is given in the figure below.

(Click on the picture for a larger image)

Figure 15. MBCAC algorithm

Our publication(s) related with this project:

 

Ç Top of page

A Realistic Mobility Model for Mobile Networks

This project was part of my PhD thesis under the supervision of Dr. Cem Ersoy.

The validity of the mobility model used to evaluate a cellular network determines the validity of the evaluation. In the literature, unrealistic assumptions on mobility are exercised for the sake of simplicity. In this paper, we present a novel mobility model which is realistic in the sense that it captures the moving-in-groups, conscious traveling, and inertial behaviors of the subscribers while respecting the  non-pass-through feature of structures like households, and preserving the autonomy of the subscribers. The mobility and call patterns of the subscribers are determined according to the locus of the subscriber over a real map. Thus, our model allows the subscribers to leave home or arrive home, walk or drive in the streets, get on the highways at specific entry points together with numerous hot and blind spots in the terrain, like city centers and lakes. The call pattern of a subscriber is effected by the type of structure he is in. The model can work on real maps to simulate the mobility patterns in real life. A  satellite image of the Anatolian side of Istanbul (with 4-m resolution) was used in our simulations.

Figure 16. Satellite image of the Asian side of Istanbul

Our publication(s) related with this project:

 

Ç Top of page