WIRELESS SENSOR NETWORK

ATS_WSN16_001 - Analysis of PKF: A Communication Cost Reduction Scheme for Wireless Sensor Networks

          Energy efficiency is a primary concern for wireless sensor networks (WSNs). One of its most energy-intensive processes is the radio communication. This work uses a predictor combined with a Kalman filter (KF) to reduce the communication energy cost for cluster-based WSNs. The technique, called PKF, is suitable for typical WSN applications with adjustable data quality and tens of picojoule computation cost. However, it is challenging to precisely quantify its underlying process from a mathematical point of view. Through an in-depth mathematical analysis, we formulate the tradeoff between energy efficiency and reconstruction quality of PKF. One of our prominent results for that is the explicit expression for the covariance of the doubly truncated multivariate normal distribution; it improves the previous methods and has generality. The validity and accuracy of the analysis are verified with both artificial and real signals. The simulation results, using real temperature values, demonstrate the efficiency of PKF: without additional data degradation, it reduces the communication cost by more than 88%. Compared to previous works based on KF, PKF requires less computational effort while improving the reconstruction quality; compared with the techniques without KF, the advantages of PKF are even more significant. It reduces the transmission rate of them by at least 29%. Besides, it can be integrated into network level techniques to further extend the whole network lifetime.

ATS_WSN16_002 - Online Packet Dispatching for Delay Optimal Concurrent Transmissions in Heterogeneous Multi-RAT Networks

          In this paper, we consider the problem of concurrent transmissions in a wireless network consisting of multiple radio access technologies (multi-RATs). That is, a single flow of packets is dispatched over multiple RATs so that the complementary advantages of different RATs can be exploited. One of the challenging issues arising in concurrent transmissions is the packet outof- order problem due to diverse wireless channel states and scheduling policies of different RATs, leading to substantial performance degradation to delay sensitive applications. To address this problem, we firstly propose a state-independent packet dispatching (SIPD) policy, which attempts to find the traffic dispatching ratios over multiple RATs to minimize the maximum average delay across different RATs in the long run. We further propose a state-dependent packet dispatching (SDPD) policy, which achieves fine-grained packet dispatching in the short-term. We use the value function as a measure of the admittance cost for packet dispatching given the current queueing states, and formulate the SDPD problem as a convex programming problem. We derive the close-form solutions for both problems for the special case of two RATs, and adopt the dual decomposition technique as the solution for the general cases. Simulation results are presented to compare the performance of the proposed schemes with existing solutions.

ATS_WSN16_003 - Toward Optimal Adaptive Wireless Communications in Unknown Environments

           Designing efficient channel access schemes for wireless communications without any prior knowledge about the nature of environments has been a very challenging issue, in which the channel state distribution of all spectrum resources could be entirely or partially stochastic or adversarial at different times and locations. In this paper, we propose an online learning algorithm for adaptive channel access of wireless communications in unknown environments based on the theory of multiarmed bandits (MAB) problems. By automatically tuning two control parameters, i.e., learning rate and exploration probability, our algorithms could find the optimal channel access strategies and achieve the almost optimal learning performance over time in different scenarios. The quantitative performance studies indicate the superior throughput gain when compared with previous solutions and the flexibility of our algorithm in practice, which is resilient to both oblivious and adaptive jamming attacks with different intelligence and attacking strength that ranges from no-attack to the full-attack of all spectrum resources. We conduct extensive simulations to validate our theoretical analysis.

ATS_WSN16_004 - Adaptive Pilot Clustering in Heterogeneous Massive MIMO Networks 

             We consider the uplink of a cellular massive MIMO network. Acquiring channel state information at the base stations (BSs) requires uplink pilot signaling. Since the number of orthogonal pilot sequences is limited by the channel coherence, pilot reuse across cells is necessary to achieve high spectral efficiency. However, finding efficient pilot reuse patterns is nontrivial especially in practical asymmetric BS deployments. We approach this problem using coalitional game theory. Each BS has a few unique pilots and can form coalitions with other BSs to gain access to more pilots. The BSs in a coalition thus benefit from serving more users in their cells, at the expense of higher pilot contamination and interference. Given that a cell’s average spectral efficiency depends on the overall pilot reuse pattern, the suitable coalitional game model is in partition form. We develop a low-complexity distributed coalition formation based on individual stability. By incorporating a base station intercommunication budget constraint, we are able to control the overhead in message exchange between the base stations and ensure the algorithm’s convergence to a solution of the game called individually stable coalition structure. Simulation results reveal fast algorithmic convergence and substantial performance gains over the baseline schemes with no pilot reuse, full pilot reuse, or random pilot reuse pattern.

ATS_WSN16_005 - Data Aggregation and Principal Component Analysis in WSNs

          Data aggregation plays an important role inWireless Sensor Networks (WSNs) as far as it reduces power consumption and boosts the scalability of the network, specially in topologies that are prone to bottlenecks (e.g. cluster-trees). Existing works in the literature use clustering approaches, Principal Component Analysis (PCA) and/or Compressed Sensing (CS) strategies. Our contribution is aligned with PCA and explores whether a projection basis that is not the eigenvectors basis may be valid to sustain a Normalized Mean Squared Error (NMSE) threshold in signal reconstruction and reduce the energy consumption. We derivate first the NSME achieved with the new basis and elaborate then on the Jacobi eigenvalue decomposition ideas to propose a new subspace-based data aggregation method. The proposed solution reduces transmissions among the sink and one or more Data Aggregation Nodes (DANs) in the network. In our simulations we consider without loss of generality a single cluster network and results show that the new technique succeeds in satisfying the NMSE requirement and gets close in terms of energy consumption to the best possible solution employing subspace representations. Additionally the proposed method alleviates the computational load with respect to an eigenvector-based strategy (by a factor of six in our simulations).

ATS_WSN16_006 - A New cost-effective approach for Battlefield Surveillance in Wireless Sensor Networks

           Assuring security (in the form of attacking mode as well as in safeguard mode) and at the same time keeping strong eye on the opposition's status (position, quantity, availability) is the key responsibility of a commander in the battlefield. Battlefield surveillance is one of the strong applications of Wireless Sensor Networks (WSNs). A commander is not only liable to his above responsibilities, but also to manage his duties in an efficient way. For this reason, ensuring maximum destruction with minimum resources is a major concern of a commander in the battlefield. This paper focuses on the maximum destruction problem in military affairs. In the work of Jaigirdar and Islam (2012), the authors proposed two novel algorithms (Maximum degree analysis and Maximum clique analysis) that ensure the efficiency and cost-effectiveness of the above problem. A comparative study explaining the number of resources required for commencing required level of destruction made to the opponents has been provided in the paper. In this paper the authors have come forward with another algorithm for the same problem. With the simulation studies and comparative analysis of the same example set the authors in this paper demonstrate the effectiveness (in both the quality and quantity) of the new method to be best among the three.