Bioans: bio-inspired ambient intelligence protocol for wireless sensor networks

This paper describes the BioANS (Bio-inspired Autonomic Networked Services) protocol that uses a novel utility-based service selection mechanism to drive autonomicity in sensor networks. Due to the increase in complexity of sensor network applications, self-configuration abilities, in terms of service discovery and automatic negotiation, have become core requirements. Further, as such systems are highly dynamic due to mobility and/or unreliability; runtime self-optimisation and self-healing is required. However the mechanism to implement this must be lightweight due to the sensor nodes being low in resources, and scalable as some applications can require thousands of nodes. BioANS incorporates some characteristics of natural emergent systems and these contribute to its overall stability whilst it remains simple and efficient. We show that not only does the BioANS protocol implement autonomicity in allowing a dynamic network of sensors to continue to function under demanding circumstances, but that the overheads incurred are reasonable. Moreover, state-flapping between requester and provider, message loss and randomness are not only tolerated but utilised to advantage in the new protocol

Self-Synchronization in Duty-cycled Internet of Things (IoT) Applications

In recent years, the networks of low-power devices have gained popularity. Typically these devices are wireless and interact to form large networks such as the Machine to Machine (M2M) networks, Internet of Things (IoT), Wearable Computing, and Wireless Sensor Networks. The collaboration among these devices is a key to achieving the full potential of these networks. A major problem in this field is to guarantee robust communication between elements while keeping the whole network energy efficient. In this paper, we introduce an extended and improved emergent broadcast slot (EBS) scheme, which facilitates collaboration for robust communication and is energy efficient. In the EBS, nodes communication unit remains in sleeping mode and are awake just to communicate. The EBS scheme is fully decentralized, that is, nodes coordinate their wake-up window in partially overlapped manner within each duty-cycle to avoid message collisions. We show the theoretical convergence behavior of the scheme, which is confirmed through real test-bed experimentation.Comment: 12 Pages, 11 Figures, Journa

Random walk with restart over dynamic graphs

Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V|3) time and O(|V|2) memory to compute all (|V|2) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V|3) to O(|Δ|) time for each update without loss of accuracy, where |Δ| (<<|V|2) is the number of affected proximities. (2) To avoid O(|V|2) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V|) is a user-controlled trade-off between  memory an I/O costs. (3) For bulk update, we also devise aggregation and hashing methods, which can discard many unneccessary updates further and handle chuncks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1-2 orders of magnitude faster than other competitors while securing scalability and exactness..

Academic Panel: Can Self-Managed Systems be trusted?

Trust can be defined as to have confidence or faith in; a form of reliance or certainty based on past experience; to allow without fear; believe; hope: expect and wish; and extend credit to. The issue of trust in computing has always been a hot topic, especially notable with the proliferation of services over the Internet, which has brought the issue of trust and security right into the ordinary home. Autonomic computing brings its own complexity to this. With systems that self-manage, the internal decision making process is less transparent and the ‘intelligence’ possibly evolving and becoming less tractable. Such systems may be used from anything from environment monitoring to looking after Granny in the home and thus the issue of trust is imperative. To this end, we have organised this panel to examine some of the key aspects of trust. The first section discusses the issues of self-management when applied across organizational boundaries. The second section explores predictability in self-managed systems. The third part examines how trust is manifest in electronic service communities. The final discussion demonstrates how trust can be integrated into an autonomic system as the core intelligence with which to base adaptivity choices upon

Co-Simmate : quick retrieving all pairwise co-Simrank scores

Co-Simrank is a useful Simrank-like measure of similarity based on graph structure. The existing method iteratively computes each pair of Co-Simrank score from a dot product of two Pagerank vectors, entailing O(log(1/e)*n^3) time to compute all pairs of Co-Simranks in a graph with n nodes, to attain a desired accuracy e. In this study, we devise a model, Co-Simmate, to speed up the retrieval of all pairs of Co-Simranks to O(log2 (log(1/e))*n^3) time. Moreover, we show the optimality of Co-Simmate among other hop-(u^k) variations, and integrate it with a matrix decomposition based method on singular graphs to attain higher efficiency. The viable experiments verify the superiority of Co-Simmate to others

Sig-SR: SimRank search over singular graphs

SimRank is an attractive structural-context measure of similarity between two objects in a graph. It recursively follows the intuition that "two objects are similar if they are referenced by similar objects". The best known matrix-based method [1] for calculating SimRank, however, implies an assumption that the graph is non-singular, its adjacency matrix is invertible. In reality, non-singular graphs are very rare; such an assumption in [1] is too restrictive in practice. In this paper, we provide a treatment of [1], by supporting similarity assessment on non-invertible adjacency matrices. Assume that a singular graph G has n nodes, with r(2+Kr2)) time for K iterations. In contrast, the only known matrix-based algorithm that supports singular graphs [1] needs O(r4n2) time. The experimental results on real and synthetic datasets demonstrate the superiority of InvSR on singular graphs against its baselines

Resource Allocation for NOMA-based LPWA Networks Powered by Energy Harvesting

In this paper, we explore perpetual, scalable, Low-powered Wide-area networks (LPWA). Specifically we focus on the uplink transmissions of non-orthogonal multiple access (NOMA)-based LPWA networks consisting of multiple self-powered nodes and a NOMA-based single gateway. The self-powered LPWA nodes use the "harvest-then-transmit" protocol where they harvest energy from ambient sources (solar and radio frequency signals), then transmit their signals. The main features of the studied LPWA network are different transmission times-on-air, multiple uplink transmission attempts, and duty cycle restrictions. The aim of this work is to maximize the time-averaged sum of the uplink transmission rates by optimizing the transmission time-on-air allocation, the energy harvesting time allocation and the power allocation; subject to a maximum transmit power and to the availability of the harvested energy. We propose a low complex solution which decouples the optimization problem into three sub-problems: we assign the LPWA node transmission times (using either the fair or unfair approaches), we optimize the energy harvesting (EH) times using a one-dimensional search method, and optimize the transmit powers using a concave-convex (CCCP) procedure. In the simulation results, we focus on Long Range (LoRa) networks as a practical example LPWA network. We validate our proposed solution and we observe a $15\%$ performance improvement when using NOMA

Quality-of-context driven autonomicity

Optimisation in wireless sensor networks is necessary due to the resource constraints of individual devices, bandwidth limits of the communication channel, relatively high probably of sensor failure, and the requirement constraints of the deployed applications in potently highly volatile environments. This paper presents BioANS, a protocol designed to optimise a wireless sensor network for resource efficiency as well as to meet a requirement common to a whole class of WSN applications - namely that the sensor nodes are dynamically selected on some qualitative basis, for example the quality by which they can provide the required context information. The design of BioANS has been inspired by the communication mechanisms that have evolved in natural systems. The protocol tolerates randomness in its environment, including random message loss, and incorporates a non-deterministic ’delayed-bids’ mechanism. A simulation model is used to explore the protocol’s performance in a wide range of WSN configurations. Characteristics evaluated include tolerance to sensor node density and message loss, communication efficiency, and negotiation latency