Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities

[EN] Fog computing is emerging an attractive paradigm for both academics and industry alike. Fog computing holds potential for new breeds of services and user experience. However, Fog computing is still nascent and requires strong groundwork to adopt as practically feasible, cost-effective, efficient and easily deployable alternate to currently ubiquitous cloud. Fog computing promises to introduce cloud-like services on local network while reducing the cost. In this paper, we present a novel resource efficient framework for distributed video summarization over a multi-region fog computing paradigm. The nodes of the Fog network is based on resource constrained device Raspberry Pi. Surveillance videos are distributed on different nodes and a summary is generated over the Fog network, which is periodically pushed to the cloud to reduce bandwidth consumption. Different realistic workload in the form of a surveillance videos are used to evaluate the proposed system. Experimental results suggest that even by using an extremely limited resource, single board computer, the proposed framework has very little overhead with good scalability over off-the-shelf costly cloud solutions, validating its effectiveness for IoT-assisted smart cities. (C) 2018 Elsevier Inc. All rights reserved.Nasir, M.; Muhammad, K.; Lloret, J.; Sangaiah, AK.; Sajjad, M. (2019). Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities. Journal of Parallel and Distributed Computing. 126:161-170. https://doi.org/10.1016/j.jpdc.2018.11.004S16117012

An analytic Study of the Key Factors Influencing the Design and Routing Techniques of a Wireless Sensor Network

A wireless sensor network contains various nodes having certain sensing, processing & communication capabilities. Actually they are multifunctional battery operated nodes called motes. These motes are small in size & battery constrained. They are operated by a power source. A wireless sensor network consists of a huge number of tiny sensor nodes which are deployed either randomly or according to some predefined distribution. The sensors nodes in a sensor network are cooperative among themselves having self-organizing ability. This ensures that a wireless network serves a wide variety of applications. Few of them are weather monitoring, health, security & military etc. As their applications are wide, this requires that sensors in a sensor network must play their role very efficiently. But, as discussed above, the sensor nodes have energy limitation. This limitation leads failure of nodes after certain round of communication. So, a sensor network suffers with sensors having energy limitations. Beside this, sensor nodes in a sensor network must fulfill connectivity & coverage requirements. In this paper, we have discussed various issues affecting the design of a wireless sensor network. This provides the readers various research issues in designing a wireless sensor network

Estimation and Improvements of the Fundamental QoS in Networks with Random Topologies

The computer communication paradigm is moving towards the ubiquitous computing and Internet of Things (IoT). Small autonomous wirelessly networked devices are becoming more and more present in monitoring and automation of every human interaction with the environment, as well as in collecting various other information from the physical world. Applications, such as remote health monitoring, intelligent homes, early fire, volcano, and earthquake detection, traffic congestion prevention etc., are already present and all share the similar networking philosophy. An additional challenging for the scientific and engineering world is the appropriateness of the alike networks which are to be deployed in the inaccessible regions. These scenarios are typical in environmental and habitat monitoring and in military surveillance. Due to the environmental conditions, these networks can often only be deployed in some quasi-random way. This makes the application design challenging in the sense of coverage, connectivity, network lifetime and data dissemination. For the densely deployed networks, the random geometric graphs are often used to model the networking topology. This paper surveys some of the most important approaches and possibilities in modeling and improvement of coverage and connectivity in randomly deployed networks, with an accent on using the mobility in improving the network functionality

No soldiers left behind: An IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information

Estimation and Improvements of the Fundamental QoS in Networks with Random Topologies

The computer communication paradigm is moving towards the ubiquitous computing and Internet of Things (IoT). Small autonomous wirelessly networked devices are becoming more and more present in monitoring and automation of every human interaction with the environment, as well as in collecting various other information from the physical world. Applications, such as remote health monitoring, intelligent homes, early fire, volcano, and earthquake detection, traffic congestion prevention etc., are already present and all share the similar networking philosophy. An additional challenging for the scientific and engineering world is the appropriateness of the alike networks which are to be deployed in the inaccessible regions. These scenarios are typical in environmental and habitat monitoring and in military surveillance. Due to the environmental conditions, these networks can often only be deployed in some quasi-random way. This makes the application design challenging in the sense of coverage, connectivity, network lifetime and data dissemination. For the densely deployed networks, the random geometric graphs are often used to model the networking topology. This paper surveys some of the most important approaches and possibilities in modeling and improvement of co verage and connectivity in randomly deployed networks, with an accent on using the mobility in improving the network functionality

SNAP : A Software-Defined & Named-Data Oriented Publish-Subscribe Framework for Emerging Wireless Application Systems

The evolution of Cyber-Physical Systems (CPSs) has given rise to an emergent class of CPSs defined by ad-hoc wireless connectivity, mobility, and resource constraints in computation, memory, communications, and battery power. These systems are expected to fulfill essential roles in critical infrastructure sectors. Vehicular Ad-Hoc Network (VANET) and a swarm of Unmanned Aerial Vehicles (UAV swarm) are examples of such systems. The significant utility of these systems, coupled with their economic viability, is a crucial indicator of their anticipated growth in the future. Typically, the tasks assigned to these systems have strict Quality-of-Service (QoS) requirements and require sensing, perception, and analysis of a substantial amount of data. To fulfill these QoS requirements, the system requires network connectivity, data dissemination, and data analysis methods that can operate well within a system\u27s limitations. Traditional Internet protocols and methods for network connectivity and data dissemination are typically designed for well-engineering cyber systems and do not comprehensively support this new breed of emerging systems. The imminent growth of these CPSs presents an opportunity to develop broadly applicable methods that can meet the stated system requirements for a diverse range of systems and integrate these systems with the Internet. These methods could potentially be standardized to achieve interoperability among various systems of the future. This work presents a solution that can fulfill the communication and data dissemination requirements of a broad class of emergent CPSs. The two main contributions of this work are the Application System (APPSYS) system abstraction, and a complementary communications framework called the Software-Defined NAmed-data enabled Publish-Subscribe (SNAP) communication framework. An APPSYS is a new breed of Internet application representing the mobile and resource-constrained CPSs supporting data-intensive and QoS-sensitive safety-critical tasks, referred to as the APPSYS\u27s mission. The functioning of the APPSYS is closely aligned with the needs of the mission. The standard APPSYS architecture is distributed and partitions the system into multiple clusters where each cluster is a hierarchical sub-network. The SNAP communication framework within the APPSYS utilized principles of Information-Centric Networking (ICN) through the publish-subscribe communication paradigm. It further extends the role of brokers within the publish-subscribe paradigm to create a distributed software-defined control plane. The SNAP framework leverages the APPSYS design characteristics to provide flexible and robust communication and dynamic and distributed control-plane decision-making that successfully allows the APPSYS to meet the communication requirements of data-oriented and QoS-sensitive missions. In this work, we present the design, implementation, and performance evaluation of an APPSYS through an exemplar UAV swarm APPSYS. We evaluate the benefits offered by the APPSYS design and the SNAP communication framework in meeting the dynamically changed requirements of a data-intensive and QoS-sensitive Coordinated Search and Tracking (CSAT) mission operating in a UAV swarm APPSYS on the battlefield. Results from the performance evaluation demonstrate that the UAV swarm APPSYS successfully monitors and mitigates network impairment impacting a mission\u27s QoS to support the mission\u27s QoS requirements

Efficient Actor Recovery Paradigm For Wireless Sensor And Actor Networks

Wireless sensor networks (WSNs) are becoming widely used worldwide. Wireless Sensor and Actor Networks (WSANs) represent a special category of WSNs wherein actors and sensors collaborate to perform specific tasks. WSANs have become one of the most preeminent emerging type of WSNs. Sensors with nodes having limited power resources are responsible for sensing and transmitting events to actor nodes. Actors are high-performance nodes equipped with rich resources that have the ability to collect, process, transmit data and perform various actions. WSANs have a unique architecture that distinguishes them from WSNs. Due to the characteristics of WSANs, numerous challenges arise. Determining the importance of factors usually depends on the application requirements. The actor nodes are the spine of WSANs that collaborate to perform the specific tasks in an unsubstantiated and uneven environment. Thus, there is a possibility of high failure rate in such unfriendly scenarios due to several factors such as power fatigue of devices, electronic circuit failure, software errors in nodes or physical impairment of the actor nodes and inter-actor connectivity problem. It is essential to keep inter-actor connectivity in order to insure network connectivity. Thus, it is extremely important to discover the failure of a cut-vertex actor and network-disjoint in order to improve the Quality-of-Service (QoS). For network recovery process from actor node failure, optimal re-localization and coordination techniques should take place. In this work, we propose an efficient actor recovery (EAR) paradigm to guarantee the contention-free traffic-forwarding capacity. The EAR paradigm consists of Node Monitoring and Critical Node Detection (NMCND) algorithm that monitors the activities of the nodes to determine the critical node. In addition, it replaces the critical node with backup node prior to complete node-failure which helps balances the network performance. The packet is handled using Network Integration and Message Forwarding (NIMF) algorithm that determines the source of forwarding the packets (Either from actor or sensor). This decision-making capability of the algorithm controls the packet forwarding rate to maintain the network for longer time. Furthermore, for handling the proper routing strategy, Priority-Based Routing for Node Failure Avoidance (PRNFA) algorithm is deployed to decide the priority of the packets to be forwarded based on the significance of information available in the packet. To validate the effectiveness of the proposed EAR paradigm, we compare the performance of our proposed work with state-of the art localization algorithms. Our experimental results show superior performance in regards to network life, residual energy, reliability, sensor/ actor recovery time and data recovery

A Low Power Architectural Framework for Automated Surveillance System with Low Bit Rate Transmission

Abstract The changed security scenario of the modern time has necessitated increased and sophisticated vigilance of the countries' borders. The technological challenges involved in accomplishing such feat of automated security system are many and require research at the components-and-algorithms as well as the architectural levels.  This paper proposes an architectural framework for automated video surveillance comprising a network of sensors and closed circuit television cameras as well as proposing algorithmic/component research of software and hardware for the core functioning of the framework, such as: communication protocols, object detection, data-integration, object identification, object tracking, video compression, threat identification, and alarm generation. In this paper, we are addressing some general topological and routing features that would be adopted in our system. There are two types of data with regard to data communication – video stream and object detection. The network is broken down into several disjoint, almost equal zones. A zone have one or more one cluster. A zone manager is chosen among the cluster heads depending on their relative residual energies. There are several levels of control that could be implemented with this arrangement with localized decision made, to get distributed effect at all levels. A cell tracks each target in its zone. If the target moves out of the range of a cell, the cell manager will send the target description to estimated next cell. The next cell starts tracking the target. If the estimated cell is wrongly chosen, corrections will be made by the cluster heads to get the new target-tracking. We also propose bitrate reduction algorithms to accommodate the limited bandwidth. One of the main feature of this paper is introducing a Low-Power Low-Bit rate video compression algorithm to accommodate the low power requirements at sensor nodes, and the low bit rate requirement for the communication protocol. We proposed two algorithms the ALBR and LPHSME. ALBR is addressing low bit rate required for sensors network with limited bandwidth which achieves a reduction in Average number of bits per Iframe by approximately 60% in case of low motion video sequences and 53% in case of fast motion video sequences . LPHSME addresses low power requirements of multi sensor network that has limited power batteries. The performance of the proposed LPHSME algorithm versus full search and three step search indicates  a reduction in motion estimation time by approximately 89% in case of low motion video sequences (e.g., Claire ) and 84% in case of fast motion video sequences. The reduced complexity of  LPHSME results in low power requirements