Dense clustered multi-channel wireless sensor cloud

Dense Wireless Sensor Network Clouds have an inherent issue of latency and packet drops with regards to data collection. Though there is extensive literature that tries to address these issues through either scheduling, channel contention or a combination of the two, the problem still largely exists. In this paper, a Clustered Multi-Channel Scheduling Protocol (CMSP) is designed that creates a Voronoi partition of a dense network. Each partition is assigned a channel, and a scheduling scheme is adopted to collect data within the Voronoi partitions. This scheme collects data from the partitions concurrently and then passes it to the base station. CMSP is compared using simulation with other multi-channel protocols like Tree-based Multi-Channel, Multi-Channel MAC and Multi-frequency Media Access Control for wireless sensor networks. Results indicate CMSP has higher throughput and data delivery ratio at a lower power consumption due to network partitioning and hierarchical scheduling that minimizes load on the network

Multiple preemptive EDCA for emergency medium access control in distributed WLANs

The increasingly use of wireless local area networks (WLANs) in public safety and emergency network services demands for a strict quality of service (QoS) guarantee especially a large number of users report an emergency for immediate channel access. Unfortunately, the traditional IEEE 802.11e-based enhanced distributed channel access (EDCA) does not support a strict QoS guarantee for life saving emergency traffic under high loads. Previous studies have attempted to enhance the performance of EDCA called the Channel Preemtive EDCA (CP-EDCA) which is a promising idea to support emergency traffic in WLANs. However, CP-EDCA supports a single emergency traffic only (i.e. no emergency service differentiation) with high delays for increased traffic loads. To overcome this problem, we propose a class of EDCA protocol called Multiple Preemption EDCA (MPEDCA) as a candidate to support multiple emergency traffics under high loads. Each MP-EDCA node can support up to four emergency traffics (life, health, property and environment) with different priorities in addition to support background (non-emergency) traffic. The proposed protocol privileged the high priority life-saving emergency traffic to preempt the services of low priority ones without much starvation in the network to maintain a strict QoS guarantee. The paper evaluates the performance of MPEDCA through an extensive analysis of simulation outcome. The results obtained show that MP-EDCA outperforms CP-EDCA in achieving lower medium access control and emergency node delays

A smart city lighting case study on an OpenStack-powered infrastructure

© 2015 by the authors; licensee MDPI, Basel, Switzerland. The adoption of embedded systems, mobile devices and other smart devices keeps rising globally, and the scope of their involvement broadens, for instance, in smart city-like scenarios. In light of this, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad hoc solutions, while at the same time taking into account valid options with regard to infrastructure management and other more advanced functionalities. Existing solutions mainly focus on core mechanisms and do not allow one to scale by leveraging infrastructure or adapt to a variety of scenarios, especially if actuators are involved in the loop. A new, more flexible, cloud-based approach, able to provide device-focused workflows, is required. In this sense, a widely-used and competitive framework for infrastructure as a service, such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks to fit the bill, replacing current application-specific approaches with an innovative application-agnostic one. This work thus describes the rationale, efforts and results so far achieved for an integration of IoT paradigms and resource ecosystems with such a kind of cloud-oriented device-centric environment, by focusing on a smart city scenario, namely a park smart lighting example, and featuring data collection, data visualization, event detection and coordinated reaction, as example use cases of such integration

Multivariate spatial condition mapping using subtractive fuzzy cluster means

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining

Vehicular Grouping Protocol: Towards Cyber Physical Network Intelligence

Vehicular network structures present a range of challenges and opportunities for efficiently managing awareness of road dynamics and network connectivity. An enhanced manageable organization can offer a better reaction to safety-related road events, facilitate dynamic topological flexibility, relate to road layout, and interact with unpredictable distribution of the vehicles. Vehicular grouping is one of the suggested structural techniques that offers a great benefit in grouping vehicles and modelling data routing, giving importance to road structure and the occurrence of a dynamic event within the associated group of vehicles. The approach discussed in this paper is based on a dynamic grouping through phases of self-formation, self-joining, self-leaving and self-healing as key components of the protocol operational cycle. Both vehicular physical connected resources and the remote computational cloud could be used for data processing and monitoring of road dynamics. This, in effect, encourages an Internet of Things (IoT) environment that enhances the dynamic performance through direct interaction between the virtualized network of vehicles and the physical network on the road leading to Internet of Vehicles (IoV). The objective of this paper is to develop a concept of network self-formation algorithm based on vehicle grouping strategy wherein the node can flexibly switch its function, be it an IoT gateway or a router node, based on the proposed fitness election model to be elected as group head. Testing using Contiki-Cooja simulator has been implemented on various road condition scenarios reflects the operational ability of the algorithm taking into consideration the network performance based on the ultimate capacity of the road