Luca Stabellini

SYNAPSE++: Code Dissemination in Wireless Sensor Networks Using Fountain Codes

This paper presents SYNAPSE++, a system for over the air reprogramming of wireless sensor networks (WSNs). In contrast to previous solutions, which implement plain negative acknowledgment-based ARQ strategies, SYNAPSE++ adopts a more sophisticated error recovery approach exploiting rateless fountain codes (FCs). This allows it to scale considerably better in dense networks and to better cope with noisy environments. In order to speed up the decoding process and decrease its computational complexity, we engineered the FC encoding distribution through an original genetic optimization approach. Furthermore, novel channel access and pipelining techniques have been jointly designed so as to fully exploit the benefits of fountain codes, mitigate the hidden terminal problem and reduce the number of collisions. All of this makes it possible for SYNAPSE++ to recover data over multiple hops through overhearing by limiting, as much as possible, the number of explicit retransmissions. We finally created new bootloader and memory management modules so that SYNAPSE++ could disseminate and load program images written using any language. At the end of this paper, the effectiveness of SYNAPSE++ is demonstrated through experimental results over actual multihop deployments, and its performance is compared with that of Deluge, the de facto standard protocol for code dissemination in WSNs. The TinyOS 2 code of SYNAPSE++ is available at http://dgt.dei.unipd.it/download.

SYNAPSE: A Network Reprogramming Protocol for Wireless Sensor Networks Using Fountain Codes

Wireless reprogramming is a key functionality in wireless sensor networks (WSNs). In fact, the requirements for the network may change in time, or new parameters might have to be loaded to change the behavior of a given protocol. In large scale WSNs it makes economical as well as practical sense to upload the code with the needed functionalities without human intervention, i.e., by means of efficient over the air reprogramming. This poses several challenges as wireless links are affected by errors, data dissemination has to be 100% reliable, and data transmission and recovery schemes are often called to work with a large number of receivers. State-of-the-art protocols, such as Deluge, implement error recovery through the adaptation of standard automatic repeat request (ARQ) techniques. These, however, do not scale well in the presence of channel errors and multiple receivers. In this paper, we present an original reprogramming system for WSNs called SYNAPSE, which we designed to improve the efficiency of the error recovery phase. SYNAPSE features a hybrid ARQ (HARQ) solution where data are encoded prior to transmission and incremental redundancy is used to recover from losses, thus considerably reducing the transmission overhead. For the coding, digital fountain codes were selected as they are rateless and allow for lightweight implementations. In this paper, we design special fountain codes and use them at the heart of SYNAPSE to provide high performance while meeting the requirements of WSNs. Moreover, we present our implementation of SYNAPSE for the Tmote Sky sensor platform and show experimental results, where we compare the performance of SYNAPSE with that of state of the art protocols.

Quantifying and Modeling Spectrum Opportunities in a Real Wireless Environment

Interference avoidance schemes based on dynamic spectrum access can be exploited by wireless sensor networks in order to mitigate the interference induced by collocated wireless devices. Developing models that can accurately describe the variations of spectrum opportunities over frequency and time is a key challenge for the energy efficient design of those access schemes. Previous works addressing this problem have considered interference-controlled environments, where a single packet flown has been artificially generated and the spectrum opportunities arising among the bursty transmissions of the considered network have been characterized. In this paper we consider instead a real environment with heterogeneous wireless devices and perform measurements for analyzing spectrum utilization over the 2.4 GHz ISM band: we statistically characterize the distribution of idle periods and investigate how spectrum occupancy varies over a time-frame of several days. The results we obtain can be of great utility while simulating the performance of different access schemes or dimensioning spectrum sensing algorithms and provide insights that can be used to select the most appropriate interference avoidance approach.

Energy-efficient detection of intermittent interference in wireless sensor networks

Communications of low-power sensor nodes are easily corrupted by transmissions of collocated devices that inducing packet losses might increase data delay and energy consumption. Dynamic Spectrum Access mechanisms can mitigate these problems. These approaches allow frequency agile sensor nodes to avoid frequency bands experiencing high interference and select channels suitable for their transmissions. In this context, detecting interference and identifying spectrum opportunities in a reliable and efficient manner becomes a task of vital importance. In this paper we propose a new interference detection algorithm accounting both for energy and complexity constraints of sensor motes as well as for the intermittent nature of interference typically experienced by sensor networks in unlicensed bands. We develop an analytical framework that allows to explicitly characterise the performance of our algorithm and also implement it on the TMote Sky sensor platform. Experimental results clearly demonstrate the capabilities of the proposed scheme to identify interfered channels.

A new probabilistic approach for adaptive frequency hopping

This paper proposes Utility Based Adaptive Frequency Hopping (UBAFH), an adaptive hopping technique implementing a new paradigm for frequency hopping systems. Traditional adaptive algorithms aim at identifying bad channels that are consequently removed from the hopset. UBAFH instead utilizes all the available frequencies but assigns different usage probabilities to different channels according to the experienced channel conditions. Opportune upper and lower limits are used in order to bound these usage probabilities and achieve a desired level of frequency diversity. We simulate the behavior of UBAFH over frequency selective fading channels and compare the achieved packet error rate and throughput with the ones of IEEE 802.15.1 and with the adaptive frequency hopping implementation proposed by IEEE 802.15.2 showing that UBAFH outperforms both approaches.

Energy-Aware Spectrum Sensing in Cognitive Wireless Sensor Networks: A Cross Layer Approach

Low-power transmissions of sensor nodes are easily corrupted by interference generated by co-located wireless terminals that leading to packet losses might increase energy consumption and result in unreliable communications. Dynamic spectrum access mechanisms can mitigate these problems allowing cognitive sensor devices to sense the spectrum and access the wireless medium in an opportunistic way. With this respect, energy efficient algorithms for spectrum sensing have to be designed in order to meet the power constraints of wireless sensor networks. In this paper we consider an energy constrained system comprising two sensor nodes that avoid interference by exploiting spectrum holes in the time domain. We design the algorithm used for spectrum sensing so as to minimize the average energy required for the successful delivery of a packet. While carrying our this task we adopt a cross layer approach that accounts for the average channel occupancy and the power of interfering transmissions at the physical layer as well as for the size of packets used by sensors at the transport layer. Our results show that accounting for the short length of packets commonly used in sensor networks can significantly improve energy efficiency leading to gains of up to 50% if compared to other spectrum sensing algorithms envisaged in the literature.

Interference aware self-organization for wireless sensor networks: A reinforcement learning approach

Reliability is a key issue in wireless sensor networks. Depending on the targeted application, reliability is achieved by establishing and maintaining a certain number of network functionalities: the greatest among those is certainly the capability of nodes to communicate. Sensors communications are sensible to interference that might corrupt packets transmission and even preclude the process of network formation. In this paper we propose a new scheme that allows to establish and maintain a connected topology while dealing with this problem. The idea of channel surfing is exploited to avoid interference; in the resulting multi-channel environment nodes discover their neighbors in a distributed fashion using a reinforcement learning (RL) algorithm. Our scheme allows the process of network formation even in presence of interference, overcoming thus the limit of algorithms currently implemented in state of the art standards for wireless sensor networks. By means of reinforcement learning the process of neighbor discovery is carried out in a fast and energy efficient way.

Analysis of networked estimation under contention-based medium access

Abstract We investigate the scalability of networked estimation under contention-based medium access. In our set-up, the state of a number of identical first-order linear plants are measured and transmitted over a shared medium. Each sensor transmits its readings to a supervisor node that maintains a continuous-time state estimate for the associated plant. When the medium access delay exceeds the sampling interval, measurements are discarded and replaced by more recent ones. Our analysis of the shared channel determines the probability of packet loss as a function of the sampling interval and the number of contending nodes. We compute the estimation distortion with periodically generated samples as a function of the packet loss rate and sampling interval, and derive a condition for stable estimator performance. We investigate the scalability limits of this stability as a function of the number of nodes. When stable estimation is possible, we provide a procedure that computes the sampling rate that minimizes the average estimation distortion. We reproduce the analysis of estimation performance when the sensors sample asynchronously according to independent Poisson counters.

Energy-aware channel selection for cognitive wireless sensor networks

Wireless sensor nodes operating in unlicensed bands can exploit frequency agility in order to mitigate, through dynamic spectrum access, the effects of interference induced by collocated networks. With this respect sensor devices can select the frequency band used for their communications in an opportunistic manner: this however requires spectrum sensing and consumes precious energy when more and more channels are sensed. In this paper we propose an algorithm allowing to limit the overhead introduced by spectrum sensing. We formulate a stochastic control problem which solution allows sensor nodes to decide whether it is better to stop the sensing procedure and transmit on a sensed channel or check a new frequency band hoping to find better interfering conditions. Simulation and experimental results show significant energy gains with respect to previously proposed channel selection algorithms that sense all the available channels.

Energy optimal neighbor discovery for single-radio single-channel wireless sensor networks

Neighbor discovery is a fundamental procedure that needs to be carried out in every wireless sensor network in order to enable communication capabilities. If nodes are mobile or multiple channels are used in the network, the same algorithm may be needed to be carried out several times during the network lifetime, consuming precious energy. In this paper we propose a way for optimizing a neighbor discovery procedure suitable for a single-radio single-channel scenario. Assuming a realistic energy model which accounts for energy required for transmitting discovery queries and listening for acknowledgements and explicitly accounting for collisions we exploit power control and the use of a contention window of variable size to minimize sensors¿ energy consumption while both transmitting and receiving. We formulate the neighbor discovery problem as a Markov decision process and through dynamic programming we compute an optimal policy defining the power level and the contention window size that must be used while broadcasting queries. This policy minimizes the energy cost of the discovery procedure for a given constraint on the maximum probability of having collisions. We further provide guidelines useful for implementing sub-optimal policies which perform asymptotically optimal for high node densities and can be computed on-line by motes with low capabilities.