Omnet++ Paper

Machine-to-machine (M2M) communication plays an important role in various kinds of intelligent networks. In this study, a hybrid cooperation scheme for data collection in hierarchical smart building networks (SBN) is proposed under the framework of M2M communications. The hierarchical network structure means that the data collection process is carried out via multi-layer communications. In the first layer, smart metres organise themselves into clusters and send information to the cluster-heads. Then all cluster-heads forward the received information to the base station automatically in the second layer.

In particular, the roles of cluster-head can be acted by either fixed nodes or user terminals in the building, and this endow a hybrid cooperation mode to the data collection process. To construct the network structure and utilise the resources efficiently, the authors first provide some theoretical analysis on the influence of network structure and bandwidth constraints. Then a distributed scheme for joint structure formation and subband allocation is proposed based on coalitional game theory. Furthermore, for the feasibility of this scheme in practical applications, some improvements of the proposed scheme have also been made at last. The advantages of the proposed scheme are verified by simulation results.

In future, a massive number of devices are expected to communicate for pervasive monitoring and measurement, industrial automation, and home/building energy management. Nevertheless, such Machine-to-Machine (M2M) communications are prone to failure due to depletion of machines energy if the communication system is not designed properly. A key step in building energy-efficient protocols for large-scale M2M communications is to assess, model or characterize a network energy consumption profile. To meet this need, we develop a theoretical and numerical framework to evaluate the cumulative distribution function (CDF) of the total energy consumption by fully exploiting the properties of stochastic geometry.

Unlike the other existing approaches, we model the transmission energy as a function of transmission power, packet size, and link affordable capacity that is a logarithmic function of experienced Signal to Interference plus Noise Ratio (SINR). Since it is very difficult, if not impossible, to derive a closed-form expression for the CDF, we derive numerically computable first- and second-order moments of energy consumption. Applying these moments we then propose Log-normal and Log-logistic distributions to approximate the CDF. Our simulation results show that Log-logistic almost precisely approximates the exact CDF.

Cloud based machine-to-machine (M2M) communications have emerged to achieve ubiquitous and autonomous data transportation for future daily life in the cyber-physical world. In light of the need of network characterizations, we analyze the connected M2M network in the machine swarm of geometric random graph topology, including degree distribution, network diameter, and average distance (i.e., hops). Without the need of end-to-end information to escape catastrophic complexity, information dissemination appears an effective way in machine swarm. To fully understand practical data transportation, G/G/1 queuing network model is exploited to obtain average end-to-end delay and maximum achievable system throughput.

Furthermore, as real applications may require dependable networking performance across the swarm, quality of service (QoS) along with large network diameter creates a new intellectual challenge. We extend the concept of small-world network to form shortcuts among data aggregators as infrastructure-swarm two-tier heterogeneous network architecture, then leverage the statistical concept of network control instead of precise network optimization, to innovatively achieve QoS guarantees. Simulation results further confirm the proposed heterogeneous network architecture to effectively control delay guarantees in a statistical way and to facilitate a new design paradigm in reliable M2M communications.

The signaling traffic in mobile networks has been increasing rapidly due to the emergence of various types of devices such as smartphones and machine-to-machine (M2M) devices. This signaling traffic heavily burdens network controllers and could trigger network failures. Therefore, the need of a scalable device management method for reducing signaling traffic volume is increasing and the standardization activities have begun in 3GPP. The current mobile networks treat all devices as smartphones and frequently control the devices in a complicated way to provide advanced user experiences, such as seamless handover and longer battery life. This is a major cause of a huge amount of signaling traffic.

However, M2M devices have different characteristics from those of smartphones, such as low-mobility, small amount data, low-frequency, and no power constraints. Therefore, a network does not require such a complicated management for M2M devices, and there is room for simplifying conventional control procedures. Moreover, smartphones often act like M2Mdevices (e.g. a smartphone is stationary when a user is in his/her home) and we believe a characteristic change aware device management method is necessary for further signaling traffic reduction. To achieve the signaling volume reduction, we propose 3GPP network parameter optimization approach in conjunction with 3GPP architectural enhancements. We evaluated the feasibility of the proposed method with a network simulator.

Due to the proliferation of applications for the Internet of Things, an increasing number of machine to machine (M2M) devices are being deployed. In particular, one of the M2M applications, video surveillance, has been widely discussed. Long Term Evolution (LTE), which can provide a high rate of data transmission and wide range of coverage, is a promising standard to serve as an M2M video surveillance system. In this paper, we study a performance maximization problem in an LTE video surveillance system.

Given a set of objects and a set of cameras, each camera has its own performance grade and its own coverage. The goal is to maximize the performance of the system by allocating limited resources to cameras while all objects should be monitored by the selected cameras. We propose a heuristic method to select the cameras and allocate resources to them to solve the problem. Moreover, to reduce the load of the LTE system, a dynamic adjustment method is also proposed.

The accommodation of machine-to-machine (M2M) terminals in mobile networks is important; therefore, future network architecture supporting M2M services is of intense interest to mobile network operators. We propose an implemental architecture of a virtualized evolved packet core (vEPC) to accommodateM2M services. The proposed architecture deploys dedicated vEPCs based on the functional requirements of services.

Every vEPC is optimized by eliminating EPC components or replacing standardized interface protocols with internal application interworking. We confirm the validity of the proposed architecture by experimentally evaluating CPU resource consumption. We also confirm that the proposed architecture reduces CPU time consumption by up to 27% by reducing signaling message volume, and improved performance is observed independently with M2M terminal mobility or communication characteristics.

Machine-to-machine (M2M) is a new type of communication which can be used in e-health, smart meters and so on. However a huge number of M2M devices will cause access congestion in third generation long-term evolution systems. It will bring access delays which cannot satisfy the requirements for the M2M deceives, especially for the devices with low tolerant delays. In this study, the authors investigate a distributed differential admission control algorithm that improves the access fairness for M2M devices with different tolerant delays.

To maximise the access fairness while maintaining the throughput, an optimisation model is established by formulating a fairness-throughput payoff function. Then, a parallel and distributed algorithm, namely, Jacobi algorithm, is introduced to solve the optimisation problem. Furthermore, the constraint of the key parameters is given to guarantee that the algorithm can converge to the optimal solution. The simulation results show that the M2Mdevices with different tolerant delays have the maximum access fairness in their own tolerant delays by using the proposed method.

With the advent of Machine to Machine (M2M) communication, we are witnessing an increased interest towards technologies that will enable efficient and reliable operation of Wireless Sensor Networks (WSN). Such networks are expected to include a large number of sensor devices which will generate large body of M2M traffic. To reduce the impact of this M2M traffic, efficient storage and retrieval methods should be employed in a distributed manner for the successful deployment of this technology.

In this paper, a distributed storage solution is presented with the aim of reducing the impact of M2Mtraffic on data centres and the network backbone. The reliable and efficient storage of the sensor data is established by taking advantage of codes with Maximum Distance Separable (MDS) properties. The solution is implanted in Contiki OS using RPL protocol [1] and its performance is evaluated through simulations. Furthermore, to realise such a system, a centralised clique finding algorithm is demonstrated and benchmarked against a solution that uses a brute force approach.

Machine to Machine (M2M) systems are actively spreading, with mobile networks rapidly evolving to provide connectivity beyond smartphones and tablets. With billions of embedded devices expected to join cellular networks over the next few years, novel applications are emerging and contributing to the Internet of Things (IoT) paradigm. The new generation of mobile networks, the Long Term Evolution (LTE), has been designed to provide enhanced capacity for a large number of mobile devices and is expected to be the main enabler of the emergence of the IoT. In this context, there is growing interest in the industry and standardization bodies on understanding the potential impact of the scalability of M2Msystems on LTE networks.

The highly heterogeneous traffic patterns of most M2M systems, very different from those of smartphones and other mobile devices, and the surge of M2M connected devices over the next few years, present a great challenge for the network. This paper presents the first insights and answers on the scalability of the IoT on LTE networks, determining to what extent mobile networks could be overwhelmed by the large amount of devices attempting to communicate. Based on a detailed analysis with a custom-built, standards-compliant, large-scale LTE simulation testbed, we determine the main potential congestion points and bottlenecks, and determine which types of M2M traffic present a larger challenge. To do so, the simulation testbed implements realistic statistical M2M traffic models derived from fully anonymized real LTE traces of six popular M2Msystems from one of the main tier-1 operators in the United States.

The interdependence between machine-type communication (MTC) and human-to-human (H2H) communication has become a major topic for the development of cellular communication systems. One example of MTC application is dynamic traffic forecast, which uses sensors that are mounted on cars as an information source (so-called extended floating car data). To reduce the impact of MTC traffic on the quality of service (QoS) of human users, this paper presents a client-controlled channel-aware transmission (CAT) strategy. A new Markovian model of the Long Term Evolution (LTE) radio resources assuming heterogeneous MTC and H2H traffic is used to evaluate the performance of this approach.

The close-to-reality parameterization of the model is achieved by laboratory LTE data rate measurement campaigns and ray-tracing analyses. The model demonstrates that the CAT scheme decreases the LTE cell utilization and improves the QoS in terms of blocking probability of H2H communication. The results regarding CAT are validated in an independent simulation and by LTE field measurements. Beyond this, the influence of different MTC traffic models, including best- and worst-case investigations, is provided.