Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

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Mobile Edge Computing: A Survey

We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.

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No need to war-drive: unsupervised indoor localization

In this paper, we introduce a framework to estimate the power consumption on switch fabrics in network routers. We propose different modeling methodologies for node switches, internal buffers and interconnect wires inside switch fabric architectures. A simulation platform is also implemented to trace the dynamic power consumption with bit-level accuracy. Using this framework, four switch fabric architectures are analyzed under different traffic throughput and different numbers of ingress/egress ports. This framework and analysis can be applied to the architectural exploration for low power high performance network router designs.

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Analysis of power consumption on switch fabrics in network routers

The majority of work in cognitive radio networks have focused on single-hop networks with mainly challenges at the physical and MAC layers. Recently, multi-hop secondary networks have gained attention as a promising design to leverage the full potential of cognitive radio networks. One of the main features of routing protocols in multi-hop networks is the routing metric used to select the best route for forwarding packets. In this paper, we survey the state-of-the-art routing metrics for cognitive radio networks. We start by listing the challenges that have to be addressed in designing a good routing metric for cognitive radio networks. We then provide a taxonomy of the different metrics and a survey of the way they have been used in different routing protocols. Then we present a case study to compare different classes of metrics. After that, we discuss how to combine individual routing metrics to obtain a global one. We end the paper with a discussion of the open issues in the design of future metrics for cognitive radio networks.

Routing Metrics of Cognitive Radio Networks: A Survey

The existence of a worldwide indoor floorplans database can lead to significant growth in location-based applications, especially for indoor environments. In this paper, we present CrowdInside: a crowdsourcing-based system for the automatic construction of buildings floorplans. CrowdInside leverages the smart phones sensors that are ubiquitously available with humans who use a building to automatically and transparently construct accurate motion traces. These accurate traces are generated based on a novel technique for reducing the errors in the inertial motion traces by using the points of interest in the indoor environment, such as elevators and stairs, for error resetting. The collected traces are then processed to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment.Implementation of the system in two testbeds, using different Android phones, shows that CrowdInside can detect the points of interest accurately with 0.2% false positive rate and 1.3% false negative rate. In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a relatively small number of traces. This number is amortized over the number of users of the building. We also discuss possible extensions to CrowdInside for inferring even higher level semantics about the discovered floorplans.

CrowdInside: automatic construction of indoor floorplans

Adding location to the available information enables a new category of applications. With the constrained battery on cell phones, energy-efficient localization becomes an important challenge. In this paper we introduce a low-energy calibration-free localization scheme based on the available internal sensors in many of today's phones. We start by energy profiling the different sensors that can be used for localization. Based on that, we propose GAC: a hybrid GPS/accelerometer/compass scheme that depends mainly on using the low-energy accelerometer and compass sensors and uses the GPS infrequently for synchronization. We implemented our system on Android-enabled cell phones and evaluated it in both highways and intra-city driving environments. Our results show that the proposed hybrid scheme has an exponential saving in energy, with a linear loss in accuracy compared to the GPS accuracy. We also evaluate the effect of the different parameters on the energy-accuracy tradeoff.

GAC: Energy-Efficient Hybrid GPS-Accelerometer-Compass GSM Localization

Adding location to the available information enables a new category of applications. With the constrained battery on cell phones, energy-efficient localization becomes an important challenge. In this paper we introduce a low-energy calibration- free localization scheme based on the available internal sensors in many of today's phones. We start by energy profiling the different sensors that can be used for localization. Based on that, we propose GAC: a hybrid GPS/accelerometer/compass scheme that depends mainly on using the low-energy accelerometer and compass sensors and uses the GPS infrequently for synchronization. We implemented our system on Android-enabled cell phones and evaluated it in both highways and intra-city driving environments. Our results show that the proposed hybrid scheme has an exponential saving in energy, with a linear loss in accuracy compared to the GPS accuracy. We also evaluate the effect of the different parameters on the energy-accuracy tradeoff.

Wireless Sensor Networks (WSNs) are vulnerable to attacks due to their limited software and hardware capabilities Energy efficient link layer jamming attacks have proven to be a real threat to wireless sensor networks This type of attacks is able to prevent network communication for relatively long periods as it does not spend a lot of power while jamming This paper proposes and evaluates two modifications to the Lightweight Medium Access Control (LMAC) [3] protocol The first is Data Packet Separation Slot Size Randomization (DS-SSR); the second is Round Robin (RR) slot size assignment The paper shows that (DS-SSR) can increase the WSN resistance against the Energy efficient denial of service link layer jamming attacks The paper also shows that employing RR slightly eliminates the negative impact on the network throughput when using countermeasures against energy efficient jamming Two measures are used to evaluate the resistance of the proposed protocol against the attack: lifetime advantage and censorship rate Experimental results show that about 8 % reduction of the attacker lifetime advantage can be achieved with DS-SSR LMAC compared to other countermeasure Furthermore, results show that the censorship rate of our proposed protocol was similar to the other countermeasures

Defending against energy efficient link layer jamming denial of service attack in wireless sensor networks

With the increasing ubiquity of mobile devices with computational capabilities comparable to many personal computers, they became the dominant personal computing devices nowadays. However, mobile devices still suffer short battery life and many of them are not capable of running rich-media, data-intensive and compute-intensive applications. Offloading to the cloud is a promising solution, however, long WAN latencies and the large amount of consumed energy using cellular data connectivity guided researchers to bring cloud capabilities to LANs to form cloudlets. On the other hand, in the last few years, there has been a new trend of making use of the computing resources of the vast amount of mobile devices available everywhere. In this paper, we introduce DroidCloudlet as a cloudlet architecture that is based on mobile devices. DroidCloudlet is empowered by available resource-rich mobile devices, while resource-constrained mobile devices can offload compute-intensive workloads to them. Offloading is carried out dynamically at runtime according to specific policies that target reducing execution time or saving energy. We also propose a lightweight cost model that is used to make offloading decision based on both context parameters and historical offloading performance results. To make use of all reachable mobile devices, we introduce a multilevel architecture in which offloading can bubble recursively from local cloudlet servers to other remote servers in any reachable cloudlet. For evaluation, we have built a prototype of the proposed architecture and performance results show that it succeeded to save up to 72% of execution time and 98% of consumed energy.

A cloudlet architecture using mobile devices

Banyan networks are commonly used as interconnection structures in ATM switches. This paper is concerned with the replication technique which was applied to the standard banyan networks. We apply this technique to the plane interconnected parallel network (PIPN) which is a switch introduced previously as a better banyan-based interconnection structure. The normalized throughput of unbuffered and buffered replicated PIPN is analyzed analytically under uniform traffic model. We apply the simulation technique to verify the analytical results under the uniform traffic model and to study the performance of different heterogeneous traffic models. The performance is shown to increase significantly when the replicated PIPN is used which supports the idea of using this switch as a new high-performance ATM switch.

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Mohamed N. El-Derini

Papers

GAC: Energy-Efficient Hybrid GPS-Accelerometer-Compass GSM Localization

GAC: Energy-Efficient Hybrid GPS-Accelerometer-Compass GSM Localization

Defending against energy efficient link layer jamming denial of service attack in wireless sensor networks

A cloudlet architecture using mobile devices

Structure and performance evaluation of a replicated banyan network based ATM switch