In this work, we assume a geographic area populated by tiny sensors, each perhaps no larger than a dime. In order to save their energy, the sensors spend most of their lifetime in sleep mode and wake up for short periods of time to participate in various tasks supportive of the overall mission of the network. We assume that the tasks to be performed stipulate QoS parameters expressed in terms of the minimum number of sensors that need to monitor their sensing area. Since only awake sensors participate in tasks, the Effective Sensor Density (ESD), defined as the density of awake sensors, is an important network parameter that obviously depends on the sleep schedules adopted in the network. The first main contribution of this work is to provide a mathematical analysis of ESD from the perspective of the monitored events. We also provide design guidelines to determine deployment-time sensor density and an associated sleep schedule that probabilistically keeps the ESD at a level needed by QoS requirements. We also propose a fully distributed sleep schedule which adaptively adjusts the duty cycles of sensors within the same sensing area based on the relative difference in their remaining energy budget. The main advantage of the proposed adaptive scheme is to balance energy consumption among sensors, thus promoting the functional longevity of the sensor network without adversely affecting the ESD.

Sensor networks have their own distinguishing characteristics that set them apart from other types of networks. Several techniques have been proposed in the literature to address some of the fundamental problems faced by a sensor network design. Most of the proposed techniques attempt to solve one problem in isolation from the others; hence, protocol designers have to face the same common challenges again and again. This, in turn, has a direct impact on the complexity of the protocols and on energy consumption. Instead of using this approach, we propose BEES, a lightweight bioinspired backbone construction protocol, that can help mitigate many of the typical challenges in sensor networks by allowing the development of simpler network protocols. We show how BEES can help mitigate many of the typical challenges inherent to sensor networks including sensor localization, clustering, and data aggregation among others.

In numerous applications of wireless sensor networks (WSN), the reliability of the data collected by sensors is cast as specific QoS requirements expressed in terms of the minimum number of sensors needed to perform various tasks. Designing a long-lived sensor network with reliable performance has always been challenging due to the modest nonrenewable energy budget of individual sensors. In such a context, energy-unaware task management protocols may result in uneven expenditure of sensor energy by assigning uneven workloads to sensors. This, in turn, often translates into reduced sensor density around those heavily loaded sensors and may, eventually, lead to the creation of energy holes that partition the network into disconnected islands. To avoid these problems and to promote network longevity, we propose two energy-aware task management protocols: our first protocol is centralized, while the second one is fully distributed. The proposed protocols assign tasks to sensors based on their remaining energy so that energy expenditure among neighboring sensors is almost even. We compare the reliable lifetime of the network achieved by assigning tasks to sensors using the proposed protocols against optimal task assignment and also against energy-unaware protocols. Extensive simulation results have revealed that the performance of the proposed protocols is very close to that of the optimal task assignment. Furthermore, our simulation has shown that the proposed protocols can increase the functional longevity of the network by about 16 percent.

In a typical sensor network environment, sensor energy is considered a precious resource that must be used wisely and only if necessary. Energy-unaware task assignment protocols can deplete the energy of some sensors much more than they do for others. This results in reducing network density around those heavily loaded sensors and eventually creates energy holes that isolate the network into separated islands. These problems have negative impacts on network durability and reliability. To avoid these problems, we propose and evaluate a lightweight management protocol that assigns tasks to sensors based on their energy so that energy consumption is almost even among network sensors. In addition, we propose another mechanism to aggregate data collected by sensors before sending them back to the base station. Our aggregation mechanism supports different aggregation functions including exact evaluation of the minimum, the maximum, and the logical OR and an approximation of the average of the collected sensory data. Using simulation, we compare the lifetime achieved by assigning tasks to network sensors using the proposed protocol against another energy- neutral protocol. Simulation results verified that the proposed approach increases network durability by balancing task load among sensors. Also simulation shows that, under reasonable parameters, the error in the approximated value of the average is less than 3%.

Although, current technology enables an inexpensive massive production of sensors, it raises numerous challenges on the protocols needed to interact with these sensors efficiently. Several techniques have been proposed to address each of these challenges individually (i.e. localization, clustering, routing, aggregation ... etc). Instead of solving each of these problems individually facing the same common challenges with each problem, we propose to construct what we call a network skeleton that is constructed immediately after network deployment and provides a topology that makes the network more tractable. The skeleton provides sensors with coarse localization information that enables them to associate their sensory data with the geographic location in which the data was measured. Moreover, it promotes a geographic routing scheme that simplifies data communication across the network through skeleton sensors. By hypothetically tiling the deployment area using identical hexagons, the construction algorithm clusters sensors based on their locations into hexagons. Skeleton sensors are chosen to be the closest sensors to the centers of these hexagons. Simulation results show that the accuracy of the proposed protocol to establish the skeleton is sufficient to make the approach applicable for most WSN applications.

We propose and evaluate a new localization protocol for sensor networks. Contrary to most current localization schemes, this protocol relies on the existence of only a single beacon node. The main idea is to hypothetically tile the deployment area with identical equilateral geometric shapes (e.g. hexagons, triangles... etc). Tiling should be such that the positions of the centers of these shapes can be easily computed relative to the position of the beacon node. After that, the set of nodes that are as close as possible to the centers of these shapes is determined. We refer to these nodes as central nodes. Basically, the positions of these central nodes can be approximated by the positions of the centers of the geometric shapes which can be calculated mathematically. These central nodes can be treated as beacons and the positions of all non-central nodes can be estimated using any range-free protocol (e.g. the centroid). Simulation results show that the accuracy of the proposed protocol is comparable to the accuracy of current localization protocols especially near the beacon node. In addition to this, the achieved localization accuracy increases significantly as the network density increases.

We investigate how to maximize network lifetime by devising a task assignment protocol that balances task load on sensors based on their remaining energy. First, we show that energy-unaware protocols can excessively assign tasks to a set of sensors more than others. Consequently, the energy of those heavily loaded sensors will be depleted much faster than lightly loaded ones. This results in reducing network density around heavily loaded sensors and eventually creates energy holes that isolate the network into separated islands. This kind of behavior has its negative impacts on network durability and reliability. To avoid these problems, we propose a management protocol that assigns tasks to sensors based on their remaining energy so that energy consumption is almost even among network sensors.