Adaptive state-based multi-radio multi-channel multi-path routing in Wireless Mesh Networks

Wireless Mesh Networks (WMNs) are envisioned to seamlessly extend the network connectivity to end users by forming a wireless backbone that requires minimal infrastructure. Unfortunately for WMNs, frequent link quality fluctuations, excessive load on selective links, congestion, and limited capacity due to the half-duplex nature of radios are some key limiting factors that hinder their deployment. To address these problems, we propose a novel Adaptive State-based Multi-path Routing Protocol (ASMRP), which constructs Directed Acyclic Graphs (DAGs) from each Mesh Router (MR) to Internet Gateways (IGWs) and effectively discovers multiple optimal path set between any given MR-IGW pair. A congestion aware traffic splitting algorithm to balance traffic over these multiple paths is presented which synergistically improves the overall performance of the WMNs. We design a novel Neighbor State Maintenance module that innovatively employs a state machine at each MR to monitor the quality of links connecting its neighbors in order to cope with unreliable wireless links. We also employ a 4-radio architecture for MRs, which allows them to communicate over multiple radios tuned to non-overlapping channels and better utilize the available spectrum. Through extensive simulations using ns-2, we observe that ASMRP substantially improves the achieved throughput (～5 times gain in comparison to AODV), and significantly minimizes end-to-end latencies. We also show that ASMRP ensures fairness in the network under varying traffic load conditions.     

Channel allocation and medium access control for wireless sensor networks

Recent developments in sensor technology, as seen in Berkeley’s Mica2 Mote, Rockwell’s WINS nodes and the IEEE 802.15.4 Zigbee, have enabled support for single-transceiver, multi-channel communication. The task of channel assignment with minimum interference, also named as the 2-hop coloring problem, allows repetition of colors occurs only if the nodes are separated by more than 2 hops. Being NP complete, development of efficient heuristics for this coloring problem is an open research area and this paper proposes the Dynamic Channel Allocation (DCA) algorithm as a novel solution. Once channels are assigned, a Medium Access Control protocol must be devised so that channel selection, arbitration and scheduling occur with maximum energy savings and reduced message overhead, both critical considerations for sensor networks. The contribution of this paper is twofold: (1) development and analysis of the DCA algorithm that assigns optimally minimum channels in a distributed manner in order to make subsequent communication free from both primary and secondary interference and (2) proposing CMAC, a fully desynchronized multi-channel MAC protocol with minimum hardware requirements. CMAC takes into account the fundamental energy constraint in sensor nodes by placing them in a default sleep mode as far as possible, enables spatial channel re-use and ensures nearly collision free communication. Simulation results reveal that the DCA consumes significantly less energy while giving a legal distributed coloring. CMAC, our MAC protocol that leverages this coloring, has been thoroughly evaluated with various modes in SMAC, a recent protocol that achieves energy savings through coordinated sleeping. Results show that CMAC obtains nearly 200% reduction in energy consumption, significantly improved throughput, and end-to-end delay values that are 50–150% better than SMAC for our simulated topologies.     

Wireless Mesh Networks: Current Challenges and Future Directions of Web-In-The-Sky

Within the short span of a decade, Wi-Fi hotspots have revolutionized Internet service provisioning. With the increasing popularity and rising demand for more public Wi-Fi hotspots, network service providers are facing a daunting task. Wi-Fi hotspots typically require extensive wired infrastructure to access the backhaul network, which is often expensive and time consuming to provide in such situations. wireless mesh networks (WMNs) offer an easy and economical alternative for providing broadband wireless Internet connectivity and could be called the web-in-the-sky. In place of an underlying wired backbone, a WMN forms a wireless backhaul network, thus obviating the need for extensive cabling. They are based on multihop communication paradigms that dynamically form a connected network. However, multihop wireless communication is severely plagued by many limitations such as low throughput and limited capacity. In this article we point out key challenges that are impeding the rapid progress of this upcoming technology. We systematically examine each layer of the network and discuss the feasibility of some state-of-the-art technologies/protocols for adequately addressing these challenges. We also provide broader and deeper insight to many other issues that are of paramount importance for the successful deployment and wider acceptance of WMNs.     

Robust Frequency Control in an Autonomous Microgrid: A Two-Stage Adaptive Fuzzy Approach

Highly intermittent power from renewable energy sources (RES) along with load and system perturbations in an autonomous microgrid (MG), results in large frequency fluctuations. Conventional controllers like PI controllers to be unable to provide acceptable performance over a wide range of operating conditions. To overcome this problem, present paper introduces a novel two-stage adaptive fuzzy logic based PI controller for frequency control of MG. In this proposed controller, particle swarm optimization (PSO) and grey wolf optimization (GWO) are used to optimize the membership functions (MFs) and rule base of fuzzy logic based PI controller. The proposed controller is examined on an MG test system, the robustness and performance of the proposed controller is tested in presence of different disturbance scenarios and parametric uncertainties. Finally, the superiority of the proposed controller is shown by comparing the results with various controllers available in literature like PSO tuned fuzzy logic based PI controller, fuzzy logic-based PI controller and also with the conventional PI controller.     

A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids

As the solar PV system (SPVS) suffered from an unavoidable complication that it has nonlinearity in I–V curves, the optimum maximum power point (MPP) measurement is difficult under fluctuating climatic conditions. For maximizing SPVS output power, MPP tracking (MPPT) controllers are used. In this paper, a new adaptive fuzzy logic controller (AFLC) based MPPT technique is proposed. In this proposed AFLC, the membership functions (MFs) are optimized using the Grey Wolf Optimization (GWO) technique to generate the optimal duty cycle for MPPT. Four shading patterns are used to experiment with the performance of the proposed AFLC. The proposed approach tracks the global MPP for all shading conditions and also enhances the tracking speed and tracking efficiency with reduced oscillations. The effectiveness and robustness of proposed AFLC based tracker results over P&O and FLC are validated using Matlab/Simulink environment. The proposed AFLC overcome the drawbacks of the classical P&O, and FLC approaches.     

Communication architecture for processing spatio-temporal continuous queries in sensor networks

Wireless sensor networks have revolutionized distributed micro-sensing because of their ease of deployment, ad hoc connectivity and cost-effectiveness. They have also enabled collecting and monitoring data from a very large area or possibly several independent areas geographically separated from each other and such a process is known as spatio-temporal data monitoring. In this paper, we define an energy-aware routing infrastructure that enables distributed query processing and supports processing of spatio-temporal queries within the network. As operator execution demands high computation capability, we propose a possible use of a heterogeneous sensor network where query operators are assigned to sparsely-deployed resource-rich nodes within a dense network of low power sensor nodes. We have designed an adaptive, decentralized, low communication overhead algorithm to determine optimal operator placement on the resource-rich nodes such that data transfer cost in the network is minimized. To the best of our knowledge, this is the first attempt to build an energy-aware communication architecture to enable in-network processing of spatio-temporal queries.