A survey study on Internet of Things resource management

Security for Wireless Sensor Networks - Configuration Aid.- Low Power Wireless Buoy Platform for Environmental Monitoring.- A Detail Performance Evaluation of the Novel Mechanisms Ensuring Maximum Connectivity and Data Transmission between Nodes, Based on the Heuristics Under 5-Color Clustered Response Approach.- Inventory Management in the Packaged Gas Industry Using Wireless Sensor Networks.- An EM-IMM Method for Simultaneous Registration and Fusion of Multiple Radars and ESM Sensors.- Locatable, Sensor-Enabled Multistandard RFID Tags.- Optimal Sensor Network Configuration Based on Control Theory.- Optimal Local Map Registration for Wireless Sensor Network Localization Problems.- Wireless Sensor Network: Application to Vehicular Traffic.- Thermal Energy Harvesting for Wireless Sensor Nodes with Case Studies.- IEEE 1451.5 Standard-Based Wireless Sensor Networks.- Fuzzy Based Optimized Routing Protocol for Wireless Sensor Networks.- Energy Aware Sensor Group Scheduling to Minimise Estimated Error from Noisy Sensor Measurements.- Smart Home for Elderly Using Optimized Number of Wireless Sensors.- Estimation of Packet Error Rate at Wireless Link of VANET.

/pdf/advances-in-wireless-sensors-and-sensor-networks-52e9wo641z.pdf

Advances in wireless sensors and sensor networks

Vehicle platooning, which is formed by a group of vehicles traveling in close proximity to one another, nose-to-tail, at highway speeds, has received considerable attention in recent years. However, though it brings many opportunities in self-driving, the security of vehicle platooning is still challenging. In this paper, to secure vehicle platooning, particularly to secure communication and efficient key updating for vehicles in a platoon, we first present the notion of ternary join exit tree. A ternary join exit tree consists of main tree, join tree, and exit tree. The users join in the join tree and leave from exit tree. Then, we propose a new dynamic ternary join-exit tree-based dynamic key management scheme, called TJET, for vehicle platooning, which is characterized by providing efficient key updating for not only vehicle joining and leaving, but also platoon merging and splitting. Specifically, based on the structure of ternary join-exit tree, we devise concrete algorithms for vehicle joining, vehicle exiting, platoon merging, and splitting. Moreover, we also analyze the capacities and activation conditions of join tree and exit tree based on strict mathematical proofs. Detailed security analysis show that our proposed TJET holds desirable security properties including forward security, backward security, and resistance to key control. In addition, performance evaluations via extensive simulations demonstrate the efficiency of TJET.

/pdf/tjet-ternary-join-exit-tree-based-dynamic-key-management-for-22ggwrb665.pdf

TJET: Ternary Join-Exit-Tree Based Dynamic Key Management for Vehicle Platooning

—Outlier detection is a subfield of data mining to determine data points that notably deviate from the rest of a dataset. Their deviation can indicate that these data points are generated by errors and should therefore be removed or repaired. There are many reasons for outliers in a network dataset such as human or instrument errors, noise or system behavior changes. On the other side, Network Behavior Analysis (NBA) is a way to monitor traffic and recognize unusual actions in a network. Analyzing data trends in NBA methods is a common way to interpret network situation. Outliers can deviate and produce erroneous trends that influence the results of the NBA methods. This paper presents an approach that based on a method for trend detection divides the data set into subsets where contextual outliers are discovered. The outliers can then be removed to have a clear dataset that better shows the network behavior when using NBA methods. Increasing the accuracy and reliability are the goals of our method. We compare the proposed method with the Hampel method on simulated IoT network data.

/pdf/an-outlier-detection-method-to-improve-gathered-datasets-for-1evxo0j8m3.pdf

An Outlier Detection Method to Improve Gathered Datasets for Network Behavior Analysis in IoT

Currently healthcare data packets do not get any special priority while routing through the Internet of Things (IoT) networks. These data packets flow through routers using conventional QoS process which does not guarantee that a patient’s critical health data traveling in congested IoT network will actually be routed to doctors office on time. This leaves the remote medical treatment at risk with possible threat to patients’ lives. In this paper, we studied the current healthcare packet routing process in IoT and its performance issues in congested IoT networks, then proposed a sensor driven solution to prioritize healthcare data routing in congested IoT networks. In our proposed system, we add a healthcare data identifier in IP packet header at the sensor level, modify QoS software at the network router level, and provide the highest priority to healthcare data packet routing based on the healthcare data identifier seen at router QoS. A prototype has been built and tested by using TI Launchpads. Test data shows that healthcare packets with an identifier can route to doctors at 80% less latency than healthcare packets routed without an identifier. Additionally, the proposed system saves medical diagnostic cost, and more importantly, reduces the risk of losing human lives. A shorter version of this analysis has also been presented to IEEE conference. The detail analysis in this paper opens up multiple avenues for further research.

Sensor Initiated Healthcare Packet Priority in Congested IoT Networks

Vehicular Ad hoc Network (VANET), as a subclass of mobile Ad hoc Networks (MANETs), is a promising approach for traffic security, mobile internet and V2V communication. While the packet loss estimation of VANET impacts routing protocol and transmission control algorithm, it turns out to be an important issue. In this paper, we propose an estimation algorithm for packet loss on VANET, RPLE (Real time packet loss estimation), which uses few probe packets and enhance the estimation accuracy than traditional methods for packet loss on wireless networks. Due to node motion and complex radio environment, packet loss estimation in VANET is a hard problem. However, packet loss estimation impacts routing protocol and transmission control algorithm of VANET, so it is an important issue. Based on mass on-road measurements, we have obtained the general statistical principals of packet loss rate (PLR) by distances on VANET, and we use Gaussian Mixture Model (GMM) to present PLR probability density. In that case, to get real-time PLR by sending a few probe packets, RPLE use maximum a posteriori probability method to estimate the current PLR based on the GMM of PLR probability density and the probing result. In real urban road scenario, the experiment results indicate that RPLE can get more accurate PLR estimation by few probe packets immediately.

Estimation of Packet Loss Rate at Wireless Link of VANET--RPLE

In this paper, we give classification for various traffics in Internet of Things according to such features as packet length and importance, and propose priority-based scheduling strategy for efficient packet transmission. Detailed theoretical analysis based on one-server queuing theory is provided to show the superiority of short-packet-first non-preemptive strategy over other strategies. And by simulation experiments in NS2, we verify the effectiveness of our method in term of such performance metrics as end-to-end delay, packet delivery ratio and processing overhead.

A packet-scheduling strategy for heterogeneous traffic of Internet of Things

For scheduling services with different features and Qos constraints, this paper proposes a hybrid scheduling strategy, in which delay-sensitive services are granted preemptive priority and non-delay-sensitive services are granted non-preemptive priority. Theoretical analysis verified that preemptive scheme results in longer waiting length of queue.

Queuing States Analysis on a Hybrid Scheduling Strategies for Heterogeneous Traffics in IOT

Node motion and complex radio environment make packet loss estimation in VANET difficult. However, packet loss estimation impacts routing protocol and transmission control algorithm of VANET, so it is an important issue. In this chapter, we measured packet error of VANET in real urban road, and analyzed the characteristics of packet error in VANET, described packet error by a packet-level Makov (PLM) model and used GMM (Gaussian Mixture Model) to present probability density of packet error, and then proposed two methods to estimate packet error, one is based on PLM model, and another is RPEE (real-time packet error estimation) which adopts GMM of probability density of packet error in VANET.

Estimation of Packet Error Rate at Wireless Link of VANET

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Papers

Estimation of Packet Loss Rate at Wireless Link of VANET--RPLE

A packet-scheduling strategy for heterogeneous traffic of Internet of Things

Queuing States Analysis on a Hybrid Scheduling Strategies for Heterogeneous Traffics in IOT

Estimation of Packet Error Rate at Wireless Link of VANET