Communication schemes exploiting dynamic spectrum access have been recently envisaged in the context of wireless sensor networks: these access techniques allow cognitive sensor devices to avoid transmissions of interfering networks by opportunistically exploiting spectrum holes in the time or frequency domain. Such spectrum opportunities need to be identified through spectrum sensing, however this might introduce significant energy overhead. Selecting the most appropriate spectrum sensing and access technique is therefore a key issue for the efficient design of these communication schemes. In this paper we present the experimental evaluation of two different cognitive approaches, respectively exploiting spectrum holes in the time and frequency domain. We quantify the average energy required by a two-node system for the successful delivery of a packet and investigate the effect of the energy overhead introduced by spectrum sensing. Our results show that the time domain approach results in poor performance even for relatively low values of channel occupancy; on the other hand channel surfing schemes introduce significant energy overhead due to spectrum sensing but allow to effectively avoid interference and should therefore be preferred to time-domain techniques when the risk of operating in heavily interfered channels is high or when a large amount of data has to be transmitted.

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.

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.

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.

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.

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.

In this paper we propose an efficient and simple method to improve system capacity in GSM/GPRS/EDGE radio systems. It exploits the power shaping concept and the parameter "TRX power reduction" already present in the standard and does not require major modifications to operators specific radio resource management algorithms. Power-shaping means assigning in advance (static allocation) a maximum power level to each channel before dynamically performing channel allocation. This is done by exploiting a set of suitably reused power profiles that partially organize the inter-cell interference in the available channels and make it partially predictable. This technique, thanks to the possibility of tightening the reuse factor, permits to increase the system capacity for both voice and data services if compared to most common reuse schemes or to the widely used concentric cells schemes. This is shown in the results where an interesting gain is achieved by power shaping with power reduction with respect to the other schemes.