Rapid Development and Flexible Deployment of Adaptive Wireless Sensor Network Applications

Wireless sensor networks (WSNs) are difficult to pro-gram and usually run statically-installed software limiting its flexibility. To address this, we developed Agilla, a new middleware that increases network flexibility while simplifying application development. An Agilla network is deployed with no pre-installed application. Instead, users inject mobile agents that spread across nodes performing application-specific tasks. Each agent is autonomous, allowing multiple applications to share a network. Programming is simplified by allowing programmers to create agents using a high-level language. Linda-like tuple spaces are used for inter-agent communication and context discovery. This preserves each agent’s autonomy while providing a rich infrastructure for building complex applications, and marks the first time mobile agents and tuple spaces are used in a unified framework for WSNs. Our efforts resulted in an implementation for MICA2 motes and the development of several applications. The implementation consumes a mere 41.6KB of code and 3.59KB of data memory. An agent can migrate 5 hops in less than 1.1 seconds with 92% reliability. In this paper, we present Agilla and provide a detailed evaluation of its implementation, an empirical study of its overhead, and a case study demonstrating its use

Towards a Versatile Problem Diagnosis Infrastructure for LargeWireless Sensor Networks

In this position paper, we address the issue of durable maintenance of a wireless sensor network, which will be crucial if the vision of large, long-lived sensornets is to become reality. Durable maintenance requires tools for diagnosing and fixing occurring problems, which can range from internode connectivity losses, to time synchronization problems, to software bugs. While there are solutions for fixing problems, an appropriate diagnostic infrastructure is essentially still lacking. We argue that diagnosing a sensornet application requires the ability to dynamically and temporarily extend the application on a selected group of nodes with virtually any functionality. We motivate this claim based on deployment experiences to date and propose a highly nonintrusive solution to dynamically extending a running application on a resource-constrained sensor node

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways

Mobile Agent Middleware for Sensor Networks: An Application Case Study

Agilla is a mobile agent middleware that facilitates the rapid deployment of adaptive applications in wireless sensor networks (WSNs). Agilla allows users to create and inject special programs called mobile agents that coordinate through local tuple spaces, and migrate across the WSN performing application-specific tasks. This fluidity of code and state has the potential to transform a WSN into a shared, general-purpose computing platform capable of running several autonomous applications at a time, allowing us to harness its full potential. We have implemented and evaluated a fire tracking application to determine how well Agilla achieves its goals. Fire is modeled by agents that gradually spread throughout the network, engulfing nodes by inserting fire tuples into their local tuple spaces. Fire tracker agents are then used to form a perimeter around the fire. Using Agilla, we were able to rapidly create and deploy 47 byte fire agents, and 100 byte tracker agents on a WSN consisting of 26 MICA2 motes. Our experiments show that the tracker agents can form an 8-node perimeter around a burning node within 6.5 seconds and that it can adapt to a fire spreading at a rate of 7 seconds per hop. We also present the lessons learned about the adequacy of Agilla’s primitives, and regarding the efficiency, reliability, and adaptivity of mobile agents in a WSN

ANALISIS PENGARUH POLA PENYEBARAN NODE PADA JARINGAN SENSOR NIRKABEL

ABSTRAK Wireless Sensor Network (WSN) merupakan infrastruktur jaringan nirkabel yang menggunakan node sensor untuk memonitor fisik atau kondisi lingkungan sekitar seperti suhu, getaran, suara. Salah satu karakteristik dari suatu jaringan sensor nirkabel adalah daya power yang terbatas. Salah satu usaha untuk menghemat konsumsi energi adalah dengan menghindari collision (tabrakan data) yang dapat terjadi ketika 2 titik sensor yang berdekatan mengirim paket ke receiver yang sama dalam waktu yang hampir bersamaan. Tabrakan data membuat receiver tidak menerima data yang dikirim sehingga berakibat packet delivery yang dihasilkan semakin rendah namun konsmsi energy yang digunakan semakin meningkat karena transmitter mengirimkan ulang sampai data tersebut terkirim. Dengan mengatur penempatan node maka akan mengatur jarak ideal antar node agar dapat mengurangi kemungkinan terjadinya collision. Pada Tugas Akhir ini dibahas pengaruh dari jumlah node aktif mengirimkan data bersamaan dan interval pengiriman paket data dengan berbagai macam tipe persebaran node yang berbeda. Performance metrics yang dianalisis adalah packet delivery ratio, throughput, delay, dan energy consumption. Protokol routing yang diggunakan dalam tugas akhir ini adalah AODV. Pemodelan dan simulasi skenario jaringan WSN dilakukan dengan bantuan software Network Simulator versi 2.35. Untuk lalu lintas pengiriman paket data menggunakan Constant Bit Rate. Sedangkan simulasi jaringan menggunakan standard IEEE 802.15.4. Berdasarkan hasil simulasi menunjukkan pada kondisi node aktif mengirimkan data bersamaan, untuk packet delivery ratio regular hexagonal menghasilkan 36.10% dan 15.82% lebih baik dari random dan grid. Untuk throughput, persebaran regular hexagonal menghasilkan 37.74% dan 18.37% lebih baik dari random dan grid. Untuk energy consumption, persebaran regular hexagonal mengkonsumsi energi 6.42% dan 11.05% lebih kecil dari random dan grid. Pada kondisi interval pengiriman paket data, untuk packet delivery ratio persebaran grid menghasilkan 12.92% dan 26.51% lebih besar dari regular hexagonal dan random. Untuk throughput, persebaran grid menghasilkan 12.16% dan 28.21% lebih baik dari regular hexagonal dan random. Kata kunci: WSN, AODV, Persebaran Random, Persebaran Grid, Persebaran Regular Hexagonal