Design considerations in wireless sensor networks

We consider three problems in the design of wireless sensor networks: cross-layer optimization, neighbor discovery, and scheduling as a method of medium access control (MAC). Cross-layer optimization will be important for sensor networks, which typically have only one or two objectives to meet. We consider a sensor network which performs decentralized detection. We devise a method in which local observations by sensors are condensed into a single bit message and forwarded to a sink node which makes a final decision. The method involves unusual interactions between the application, the routing function, and the physical layer. Neighbor discovery is useful in sensor networks whose nodes are immobile, since routing and scheduling algorithms can make good use of neighbor information. We propose an asynchronous neighbor discovery algorithm. The algorithm is probabilistic: each node obtains a list of its neighbors which is possibly incomplete. Performance is analyzed and optimal parameter settings are obtained. Scheduling deserves consideration as a MAC in sensor networks, because MACs based on contention methods waste energy in retransmissions. We state a natural centralized scheduling problem, in which link demands are to be satisfied under signal-to-interference-and-noise-ratio (SINR) constraints, and transmit powers may be varied. We show that solving this minimum length scheduling problem is at least as hard as another problem we define, MAX-SINR-MATCHING, in the sense that if there is no polynomial-time algorithm to solve the latter then there is no polynomial-time algorithm to solve the former. We give evidence that MAX-SINR-MATCHING is a difficult problem. We add several theorems on the SINR model which exploit algebraic structure. The theorems predict what sets of links could be simultaneously activated in a wireless network and depend only on the SINR requirements of the nodes and the worst propagation loss in a network. These theorems apply to all wireless networks which can be described by SINR requirements, not only to sensor networks

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Panda: Neighbor Discovery on a Power Harvesting Budget

Object tracking applications are gaining popularity and will soon utilize Energy Harvesting (EH) low-power nodes that will consume power mostly for Neighbor Discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailored for the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in which nodes transition between the sleep, receive, and transmit states. We analyze \name and select its parameters to maximize the ND rate subject to a homogeneous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs. We demonstrate a small difference (less then 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3x compared to related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting

Topology Construction in RPL Networks over Beacon-Enabled 802.15.4

In this paper, we propose a new scheme that allows coupling beacon-enabled IEEE 802.15.4 with the RPL routing protocol while keeping full compliance with both standards. We provide a means for RPL to pass the routing information to Layer 2 before the 802.15.4 topology is created by encapsulating RPL DIO messages in beacon frames. The scheme takes advantage of 802.15.4 command frames to solicit RPL DIO messages. The effect of the command frames is to reset the Trickle timer that governs sending DIO messages. We provide a detailed analysis of the overhead incurred by the proposed scheme to understand topology construction costs. We have evaluated the scheme using Contiki and the instruction-level Cooja simulator and compared our results against the most common scheme used for dissemination of the upper-layer information in beacon-enabled PANs. The results show energy savings during the topology construction phase and in the steady state

Talk More Listen Less: Energy-Efficient Neighbor Discovery in Wireless Sensor Networks

Neighbor discovery is a fundamental service for initialization and managing network dynamics in wireless sensor networks and mobile sensing applications. In this paper, we present a novel design principle named Talk More Listen Less (TMLL) to reduce idle-listening in neighbor discovery protocols by learning the fact that more beacons lead to fewer wakeups. We propose an extended neighbor discovery model for analyzing wakeup schedules in which beacons are not necessarily placed in the wakeup slots. Furthermore, we are the first to consider channel occupancy rate in discovery protocols by introducing a new metric to trade off among duty-cycle, latency and channel occupancy rate. Guided by the TMLL principle, we have designed Nihao, a family of energy-efficient asynchronous neighbor discovery protocols for symmetric and asymmetric cases. We compared Nihao with existing state of the art protocols via analysis and real-world testbed experiments. The result shows that Nihao significantly outperforms the others both in theory and practice.Comment: 9 pages, 14 figures, published in IEEE INFOCOM 201

Using combined keying materials for key distribution in wireless sensor networks

In this paper, we propose a probabilistic key predistribution scheme for wireless sensor networks that increases connectivity of the basic scheme while keeping sizes of keyring and key pool fixed. We introduce the concept of XORed key, which is the bitwise XOR of two regular (a.k.a. single) keys. Sensor nodes are preloaded with a mixture of single and XORed keys. Nodes establish secure links by using shared XORed keys whenever possible. If node pairs do not have any shared XORed or single keys, they transfer keys from their secure neighbors in a couple of ways, and use them to match with their XORed keys. In this way, the probability of securing links, i.e. local connectivity, increases. The decision of which key is to be transferred from which node is given based on local information at the hand of the nodes. We aim to control the resilience of the network against node capture attacks by using XORed keys since an attacker has to know either both single key operands or the XORed key itself. Simulations show that our scheme is up to 50% more connected as compared to basic scheme. Also it has better resilience performance at the beginning of a node capture attack. When it starts to deteriorate, the difference between the resilience of our proposed scheme and basic scheme is not greater than 5%