Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer

Internet of Things (IoT) networks are mostly comprised of power-constrained devices, therefore the most important consideration in designing IoT applications, based on sensor networks is energy efficiency. Minor improvement in energy conservation methods can lead to a significant increase in the lifetime of IoT devices and overall network. To achieve efficient utilisation of energy, different solutions are proposed such as duty cycling optimization, design changes at the MAC layer, etc. In this paper, we propose a new approach to overcome this challenge in cloud-based IoT sensing applications, based on integration of an abstraction layer with constrained application mechanism. To achieve energy conservation and efficient data management in IoT sensing applications, we incorporate modules of efficient web framework with cloud services, in order to minimize the number of round trips for data delivery and graph-based data representation. Our study is the first attempt in the literature, to the best of our knowledge, which introduces the potential of this integration for achieving the aforementioned objectives in the target applications. We implemented the proposed interfacing of abstraction layer in constrained applications, to develop a testbed using Z1 IoT motes, Contiki OS and GraphQL web framework with Google cloud services. Experimental comparisons against baseline REST architecture approach show that our proposed approach achieved significant reductions in data delivery delay and energy consumption (minimum 51.53% and 52.88%, respectively) in IoT applications involving sensor network.</jats:p

User Transmit Power Minimization through Uplink Resource Allocation and User Association in HetNets

The popularity of cellular internet of things (IoT) is increasing day by day and billions of IoT devices will be connected to the internet. Many of these devices have limited battery life with constraints on transmit power. High user power consumption in cellular networks restricts the deployment of many IoT devices in 5G. To enable the inclusion of these devices, 5G should be supplemented with strategies and schemes to reduce user power consumption. Therefore, we present a novel joint uplink user association and resource allocation scheme for minimizing user transmit power while meeting the quality of service. We analyze our scheme for two-tier heterogeneous network (HetNet) and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms compared to 20 dBm in state-of-the-art Max reference signal received power (RSRP) and channel individual offset (CIO) based association schemes

Predicting machine behavior from Google cluster workload traces

Data centers today host a number of computational resources to support the increasing demand for computation and storage. Understanding how these physical and virtual machines transition between different states of operation (referred to as machine lifecycle) enables more efficient data center operation management. Furthermore, it helps data center operators define policies on how new computational resources can be added or existing infrastructure decommissioned. Using Google cluster trace data set version 3 collected from approximately 96 k machines, we analyze machine failure and changes in machine lifecycle over time. We observed that there is a 13% chance of another machine failure under the same network switch within 1 min of the previous machine failure. A Markov chain-based model is proposed, that can predict machine states at any given time. Using the model and estimated probabilities, we predicted the machine state over a span of several days with a high probability. Using the predicted machine state, we reconstructed the active machines trend and compared this with the trend reported in the data set, observing an error of 1.76%

An Adapting Random Walk for Ad hoc and Sensor Networks

In this paper we propose an adaptive random walk for wireless networks. The lifetime of the walk is varied in such a way that at least a given fraction of nodes is covered, in expectation. The only parameter of the random walk, a, depends on the nominal network size N and on the required coverage. For sizes lower than N the required coverage is satisfied with lifetime slightly higher than the optimal one. The paper reports performance results obtained through analytical study backed up with simulations

On the coverage process of random walk in wireless ad hoc and sensor networks

Random walk (RW) is simple to implement and has a better termination control. The Markov chain analysis informs that RW eventually visits all vertices of a connected graph. Due to such nice properties, RW is often proposed for information dissemination or collection from all or part of a large scale unstructured network. The random walker, which can be used to disseminate or collect information, visits the nodes while selecting randomly one of the neighbors. The selection of neighbors is effected by the neighbor density or the connectivity degree of the nodes. The connectivity degree in turn depends on the radius of transmission of wireless nodes. In this paper we studied the coverage process of the RW on random geometric graph. The random geometric graphs are often considered as a model for wireless ad hoc and sensor networks. We defined and studied a metric called "attenuation" that indicates how fast a RW can move in the network while disseminating or collecting information. We showed that attenuation depends on the topology, the number of nodes in a network and the transmission radius of the nodes. We then studied the effect of attenuation on the RW coverage process analytically and through simulations and showed that attenuation is the normalized estimated search time of the network. In the end we applied the results obtained to show that the estimated search time in random geometric graphs is proportional to the reciprocal of the number of replicated targets. ©2010 IEEE

A robust and energy efficient protocol for randomwalk in ad hoc networks with IEEE 802.11

This paper is about energy efficient and robust implementation of random walks in mobile wireless networks. While random walk based algorithm are often proposed to solve many problems in wireless networks, their implementation is usually done at the application layer so that many characteristics of the wireless transmissions are not exploited. In this paper we show that we can greatly reduce the energy requirements to perform a walk by better exploiting the broadcast nature of the transmissions. We propose a robust, energy efficient distributed next hop selection algorithm. To evaluate the algorithm we present a simulation study performed with ns-2. We found that in the proposed algorithm energy is reduced to more than 4 times and the selection delay is reduced to more than 8 times as compared to a standard next hop selection implementation. ©2008 IEEE