Pirabakaran Navaratnam

Resource Reservation Schemes for IEEE 802.11-Based Wireless Networks: A Survey

IEEE 802.11-based wireless technology is widely applied in many areas, supporting communications where wired devices are not available. However, providing satisfactory QoS is still a challenging topic in 802.11-based wireless networks because of the problems such as error-prone wireless channel condition, power consumption, short of centralised facility, mobility as well as channel contention. For addressing these issues, one feasible solution can be to implement resource reservation for the sessions that require QoS assurances. The responsibility of resource reservation scheme is to make sure that QoS-sensitive sessions get sufficient bandwidth in order to sustain their high performance. Difficulties are already identified for designing resource reservation schemes in both network and MAC layers. However, there is no profound investigation outcome for this kind of QoS mechanism. Therefore, in this paper, we intend to produce a comprehensive survey of resource reservation approaches for IEEE 802.11-based wireless networks. The associated research works are summarised and also classified. Moreover, both the drawbacks and the merits of each kind of resource reservation scheme are highlighted.

In-network caching of Internet-of-Things data

The recent forecast of billions of devices, all connected to the Internet and generating low-rate monitoring, measurement, or automation data that many end-users/applications frequently request, signifies the need for applying in-network caching techniques to Internet-of-Things (IoT) traffic. Although time delay is not critically important for small-sized IoT content, the expected total traffic load on the Internet from a large number of devices is significant. However, the main challenge as opposed to the typically cached content at content routers, e.g. multimedia files, is that IoT data are transient and therefore require different caching policies. This paper studies in-network caching of IoT data at content routers in the Internet. An IoT data item is uniquely defined not only by its time and location tags, but also a time-range value set by end-users/applications. We provide a model for the trade-off between multihop communication costs and the freshness of a transient data item. Results show that the model can successfully capture the effect of data transiency and can accurately represent the expected gains of a caching system: considerable savings in terms of reduction of network load, especially for highly requested data items.

On the performance of DCCP in wireless mesh networks

The Datagram Congestion Control Protocol (DCCP) has been recently proposed as a new transport protocol, suitable for use by applications such as multimedia streaming. Wireless mesh networks have promising commercial potential for a large variety of applications. In this paper, we evaluate the performance of DCCP with TCP Friendly Rate Control (TFRC) in wireless mesh networks using ns2 simulations, in terms of fairness and throughput smoothness. Our results show that in wireless mesh networks DCCP shares the limited wireless channel bandwidth fairly with the competing flows and provides better throughput smoothness than TCP flows in isolation i.e. with no competing flows. However, DCCP loses its ability to maintain the smoothness for streaming media applications with competing flows in the network.

TAN: A Distributed Algorithm for Dynamic Task Assignment in WSNs

We consider the scenario of wireless sensor networks where a given application has to be deployed and each application task has to be assigned to each node in the best possible way. Approaches where decisions on task execution are taken by a single central node can avoid the exchange of data packets between task execution nodes but cannot adapt to dynamic network conditions, and suffer from computational complexity. To address this issue, in this paper, we propose an adaptive and decentralized task allocation negotiation algorithm (TAN) for cluster network topologies. It is based on noncooperative game theory, where neighboring nodes engage in negotiations to maximize their own utility functions to agree on which of them should execute single application tasks. Performance is evaluated in a city scenario, where the urban streets are equipped with different sensors and the application target is the detection of the fastest way to reach a destination, and in random WSN scenarios. Comparisons are made with three other algorithms: 1) baseline setting with no task assignment to multiple nodes; 2) centralized task assignment lifetime optimization; and 3) a dynamic distributed algorithm, DLMA. The result is that TAN outperforms these algorithms in terms of application completion time and average energy consumption.

A link adaptive transport protocol for multimedia streaming applications in multi hop wireless networks

Transport layer performance in multi hop wireless networks has been greatly challenged by the intrinsic characteristics of these networks. In particular, the nature of congestion, which is mainly due to medium contention in multi hop wireless networks, challenges the performance of traditional transport protocols in such networks. In this paper, we first study the impact of medium contention on transport layer performance and then propose a new transport protocol for improving quality of service performance in multi hop wireless networks. Our proposed protocol, Link Adaptive Transport Protocol provides a systemic way of controlling transport layer offered load for multimedia streaming applications, based on the degree of medium contention information received from the network. Simulation results show that the proposed protocol provides an efficient scheme to improve quality of service performance metrics, such as end-to-end delay, jitter, packet loss rate, throughput smoothness and fairness for media streaming applications. In addition, our scheme requires few overhead and does not maintain any per-flow state table at intermediate nodes. This makes it less complex and more cost effective.

Semantic sensor service networks

Sensors and sensor networks are fundamental instruments to acquire and communicate contextual information from the physical world. This information enables better understanding of the physical world for humans and supports creation of ambient intelligence for a wide range of applications in different domains such as smart cities, healthcare and intelligent transportation. To facilitate scalable and seamless access and management of the information obtained from large and heterogeneous sensor networks, we introduce the concept of Semantic Sensor Service Networks which brings the research on Semantic Sensor Network one-step further. The concept combines semantic modelling, knowledge management and service-oriented design to support sensor service access, discovery and composition. It defines a framework in which sensor services can collaborate and co-operate to support data integration from different sensor networks and service computing for complex business applications.

Distributed Resource Reservation Mechanism for IEEE 802.11e-Based Networks

Resource reservation is one of the effective solutions in guaranteeing the quality-of-service (QoS) in communication networks. In this paper, we propose a novel resource reservation scheme for dynamically managing the bandwidth resources in IEEE 802.11e-based wireless local area networks (WLANs). The proposed solution consists of two components namely (i) dynamic medium access control (MAC) scheduler and (ii) adaptive admission control algorithm. Once resources occupied by certain stations become idle due to the end of QoS data session, the proposed MAC scheduler will either release the resources to the non-real-time traffic flows (NRTTFs) or re-allocate them to other real-time traffic flows (RTTFs) which require dedicated resources. The re-allocating process is implemented with the support of the novel adaptive admission control algorithm (AACA) that decides which RTTF can gain access to the available resources in a distributed manner. Evaluation of our proposed scheme is conducted in several scenarios through extensive ns2 simulation. The simulation results indicate that the proposed distributed resource reservation scheme significantly improves the efficiency of resource distribution with real-time and non-real time traffic flows in WLANs, while guaranteeing the QoS for the admitted RTTFs.

CAINE: a context-aware information-centric network ecosystem

Information-centric networking (ICN) is an emerging networking paradigm that places content identifiers rather than host identifiers at the core of the mechanisms and protocols used to deliver content to end users. Such a paradigm allows routers enhanced with content-awareness to play a direct role in the routing and resolution of content requests from users, without any knowledge of the specific locations of hosted content. However, to facilitate good network traffic engineering and satisfactory user QoS, content routers need to exchange advanced network knowledge to assist them with their resolution decisions. In order to maintain the location-independency tenet of ICNs, such knowledge (known as context information) needs to be independent of the locations of servers. To this end, we propose CAINE - Context-Aware Information-centric Network Ecosystem - which enables context-based operations to be intrinsically supported by the underlying ICN routing and resolution functions. Our approach has been designed to maintain the location-independence philosophy of ICNs by associating context information directly to content rather than to the physical entities such as servers and network elements in the content ecosystem, while ensuring scalability. Through simulation, we show that based on such location-independent context information, CAINE is able to facilitate traffic engineering in the network, while not posing a significant control signalling burden on the network.

Energy-Efficient Clustering for Wireless Sensor Networks with Unbalanced Traffic Load

Clustering algorithms have been extensively applied for energy conservation in wireless sensor networks (WSNs). In a clustered WSNs, cluster-heads (CHs) play an important role and drain energy more rapidly than other member nodes. Numerous mechanisms to optimize CH selection and cluster formation during the set-up phase have been proposed for extending the stable operation period of the network until any node depletes its energy. However, the existing mechanisms assume that the traffic load contributed by each node is the same, in other words, same amount of data are sent to CH from the member nodes during each scheduled round. This paper assumes the nodes contribute traffic load at different rates, and consequently proposes an energy-efficient clustering algorithm by considering both the residual node energy and the traffic load contribution of each node during the set-up phase. The proposed algorithm makes nodes with more residual energy and less traffic load contribution get more chances to become CHs. Furthermore, clusters are adaptively organized in a way that the deviation of ratio between the total cluster energy and the total cluster traffic load (ETRatio) is limited, in order to balance the energy usage among the clusters. Performance evaluation shows that the proposed algorithm extends the stable operation period of the network significantly.

Cooperative task assignment for distributed deployment of applications in WSNs

Nodes in Wireless Sensor Networks (WSNs) are becoming more and more complex systems with the capabilities to run distributed structured applications. Which single task should be implemented by each WSN node needs to be decided by the application deployment strategy by taking into account both network lifetime and execution time requirements. In this paper, we propose an adaptive decentralised algorithm based on noncooperative game theory, where neighbouring nodes negotiate among each other to maximize their utility function. We then prove that an increment of the nodes utility corresponds to the same increment of the utility for the whole network. Simulation results show significant performance improvement with respect to existing algorithms.