Wireless Sensor Networks (WSNs) are generally energy and resource constrained. However, packet level reliability is an important quality-of-service metric for any networking application. Existing sensor networking protocols that provide reliable data transfer for sensor-to-sink traffic either provide reliability at the event level or are not energy-efficient. Employing duty cycles improves energy-efficiency but significantly degrades the network performance, particularly packet delivery ratio for higher network traffic. We propose a cross layered approach that utilizes inactive nodes as monitors to assist in quick packet-loss detection and recovery while employing duty cycles for energy-efficiency. To improve packet level reliability, monitors passively overhear the communication of active nodes for detecting packet losses and also act as alternate forwarders to overcome congested spots. We implemented the proposed approach in ns-2 simulator and conducted extensive experimentation: results show that monitors provide a significant improvement in packet delivery ratio while providing energy savings.

Wireless Sensor Networks (WSNs) consist of spatially-distributed autonomous sensors that can cooperatively monitor physical and environmental conditions. Because of sensors' resource-constraints in terms of size, power, and bandwidth, one of the fundamental objectives in WSNs is improving energy-efficiency. In this paper, utilizing sensors with variable sensing radii, we propose a group-based technique to obtain energy-efficient k-coverage based on our previous work with the Delaunay-Triangulation-based I-coverage algorithm. Our sensing-radii optimization technique ensures full coverage and attains nearly-optimal energy consumption in sensing. Furthermore, our ns-2 simulations confirm that the group-based k-coverage reduces sensing energy consumption and maintains a sound coverage ratio for reliable surveillance.

Wireless sensor networks (WSNs) consist of spatially-distributed autonomous sensors that can cooperatively monitor physical and environmental conditions. Because of sensors' resource-constraints in terms of size, power, and bandwidth, one of the fundamental objectives in WSNs is improving energy-efficiency. In this paper, we propose a mesh-based technique to obtain energy-efficiency in sensing and improve the quality of coverage. Our technique dynamically schedules a set of active sensors based on a square mesh or an equilateral-triangular mesh. The purpose of our technique is to quickly build an energy-efficient mesh that is self-adaptive to the local topology and allows energy balancing. Furthermore, a hole detection and recovery mechanism is discussed to guarantee full coverage. The simulation results confirm that our mesh-based coverage technique reduces sensing energy consumption and maintains a sound coverage ratio for reliable surveillance.

Wireless sensor networks (WSNs) are generally used for harsh environments involving military surveillance, emergency response, and habitat monitoring. Due to severe resource constraints in sensor nodes, including memory space, energy storage, and communication bandwidth, a need arises for an in-network aggregation of sensory data. We propose a sensor- to-sink transport protocol, which is suitable for data aggregation and provides reliable upstream packet delivery by dynamically configuring inactive nodes as "monitors" to assist in quick loss detection and recovery. To ensure energy efficiency, a minimum set of monitors is preferred; however, the problem of finding a minimum set of monitors is NP-complete. Thus, we propose a distributed greedy heuristic to solve this problem efficiently. Our ns-2-based simulations show significant performance improvements over other transport schemes in terms of throughput and data delivery rate under scenarios with intermittent traffic load and unpredictable node failures.

Localization is the process in which nodes in a wireless sensor network self-determine their positions in the network. While there are many effective mathematical techniques for solving the problem of localization, most are not suitable for the resource-constrained distributed environment of sensor networks. We propose ANIML an iterative, range-aware relative localization technique for wireless sensor networks that requires no anchor nodes. ANIML restricts itself to the use of only local 1- and 2-hop neighbor information, avoiding the need for information flooding and thus controlling cascading ranging errors that bedevil other localization techniques. While least-squares minimization is a mathematically simple constraint optimization technique, utilizing 1- and 2-hop neighbor information as constraints, ANIML provides better localization without the need for more sophisticated error control and/or global information. We implemented ANIML in ns-2 and conducted extensive experimentation to evaluate its performance. Experimental results show that ANIML provides robust localization and scales well.

Desirable features of routing protocols for mobile ad hoc networks (MANETs) include ability to adapt to changing network conditions due to mobility and provide quality control mechanisms during the life time of a route. Current routing protocols that provide quality of service (QoS) for MANETs have proposed routing based on a single QoS metric. This paper proposes a QoS aware source initiated ad-hoc routing protocol (QuaSAR) that adds quality control to all the phases of an on-demand routing protocol. QuaSAR gathers information about battery power, signal strength, bandwidth and latency during route discovery and uses in route choosing. Additionally, our approach has proactive route maintenance features in addition to the reactive maintenance. We conducted simulation experiments using ns-2 network simulator and compared our results with dynamic source routing (DSR). Our performance results demonstrate that our technique has increased throughput and packet delivery ratio.

A cellular network is often modelled as a graph and the channel assignment problem is formulated as a coloring problem of the graph. We introduce the notion of cellular graphs that models the hexagonal cell structures of a cellular network. Exploiting the regular structure of the cellular graphs we compute the upper and the lower bounds for a class of channel assignment problems. Assuming a k-band buffering system where the interference does not extend beyond k cells away from the call originating cell, we provide two different formulations of the channel assignment problem-distance-k chromatic number problem and k-band chromatic bandwidth problem. We give one algorithm for the first problem and two for the second, with all three algorithms assigning channels to the cells. The complexity of the algorithm for the first problem is O(p), where p is the number of cells. For the second problem, the complexity of the first algorithm is O(p) and the complexity of the second algorithm is O(k/sup 5/log k). All the algorithms are asymptotically optimal, in the sense that the order of the upper bound of the number of channels required is the same as the order of the lower bound.