Chapter 4
Distributed Sensor Networks
Deployment of Multi-Agent Systems in Distributed Sensor Networks
Domains and Networks 128
The Sensor Node 128
The Sensor Network 129
Further Reading 143
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Chapter 4. Distributed Sensor Networks
128
The growing complexity of computer networks and their heterogeneous
composition of devices ranging from high-resources server to low-resource
mobile devices demands for unique and standardized new data processing
paradigms and methodologies. The Internet-of-Things is one major example
rising in the past decade, strongly correlated with Cloud Computing and Big
Data concepts.
4.1 Domains and Networks
The sensor network is partitioned into domains. Each network node creates
a local domain that can bind multiple communication end-points. Multiple
network nodes can be grouped in domains. Single nodes can act as bridges
between domains. The network can be considered as being a global domain
with independent sub-domains. Networks can be hierarchical, creating group-
and graph-network domains.
4.2 The Sensor Node
All services of a sensor node are provided by stationary non-mobile service
agents, shown in Figure 4.1. There is a root agent, the node service agent,
which is responsible to set up and start other service agents like the event
manager and the sensor sampler agent.
Fig. 4.1 Stationary agents (green( on sensor node level providing node OS services
interacting with mobile agents (yellow) using the tuple space data base
Tuple Space
Database
Node Service
Sensor
Sampler
Event
Manager
Energy
Manager
Resource
Manager
Data
Distribution
Feature
Recognition
Data
Distribution
Feature
Recognition
Notification
Notification
(SENS,0,1024)
(SENS,2,599)
(MARK,567)
(NOTIFY,-2,2)
Operating
Services
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4.3 The Sensor Network 129
The node service agent monitors the other node service agents. If they are
in an unresponsive or failure state, the node service agent restarts the service
agents to ensure node services availability.
4.3 The Sensor Network
A distributed sensor network as the central part of a sensing system con-
sists of multiple active sensor nodes, a set N={n
1
, n
2
, ..}, connected and
arranged in a network forming a graph G(N,C) with edges C, a set C={c
1
, c
2
,
..}, connecting nodes and providing communication (and eventually energy
transfer) between nodes. Each sensor node provides signal and data process-
ing for a set of sensors S={s
1
, s
2
, ..}, communication, some kind of energy
supply and management, and sensor interfaces. The connectivity of the sen-
sor nodes can be used for data and power exchange, too. Each sensor node
should provide a minimal degree of autonomy and independence from other
nodes in the network.
From the computer science point of view a sensor network can be com-
posed of a set of processes P={p
1
, p
2
, ..} performing parallel data processing
and interaction using messages M={m
1
, m
2
,..}. The processes communicate
by exchanging data by using messages, and messages are exchanges by the
nodes hosting the processes by using communication links. Message-based
communication requires protocols for synchronization and message passing.
Thus, in this sense and assuming the assumption of the above definition a dis-
tributed sensor network can be considered as being a distributed computer
performing distributed data processing. Interaction between nodes is
required to manage and distribute data and to compute information.
The properties and behaviour of a distributed system can be summarized
as follows (see Figure 4.1):
1. Processing elements (PE) represent the physical resources and the
nodes of the sensor network;
2. Processes are logical resources that perform the data processing for a
specific task;
3. Cooperative communication and inter-process communication han-
dled by message passing (instead of a centralized master-slave model);
4. Communication links (the interconnection network) are physical
resources and connects PEs;
5. Processes are cooperative by interaction;
6. Communication delay does not affect the overall distributed program
behaviour (immutability);
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Chapter 4. Distributed Sensor Networks
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7. Robustness in the presence of resource failures (failures of single logi-
cal or physical resource do not affect overall system behaviour);
8. Run-time reconfiguration: adaptation of single resources or the whole
system in the presence of resource failures.
Of these physical and logical elements, specifically 1, 2 and 4 are prone to fail.
4.3.1 Communication and Network Topologies
Communication is a central part of distributed data processing in sensor
networks, influencing performance and operation significantly. Figure 4.2
(top) gives an overview of different network topologies providing connectivity
between sensor or computational nodes, covering heterogeneous networks
with computation platforms differing in computational and storage resources
(including generic computers), too.
A connection between two nodes is usually a bidirectional (serial) link,
which is capable to transfer data encapsulated in messages. Message passing
is the preferred communication method for large scale networks compared
with switched networks requiring crossbar or multi-stage Banyan (butterfly)
switches. Path finding and routing, the process of forwarding a message in the
network with the goal to deliver messages from the sender to the receiver is
required in message passing networks, and for economic reasons and the
sake of simplicity there are usually no dedicated routers in sensor networks.
Thus, a sensor or computational node is a service end-point and a message
router, too. Traditional computer networks use unique node identification
that ate unsuitable for (especially loosely or ad-hoc coupled) sensor networks.
Path finding algorithms can be classified in those using routing tables man-
aged by each network node and storing information about the network
topology (usually limited to the neighbourhood of the respective node), and
those not relying on routing tables. Routing tables provide information for
path planning in advance. Managing routing tables can be critical in resource
constrained and real-time systems, because they allocate a fairly high amount
of local storage and require computational and communication activities,
which should be minimized in sensor networks. Routing tables can only reflect
an outdated view of the network (the network configuration within a certain
time interval) and hence dealing with network changes is complicated by
using routing tables.
Communication links can be classified fundamentally in wired and wireless
technologies, and networks themselves in message passing (static) and
switched (dynamic) networks with regular and irregular topologies of a spe-
cific dimensionality (see Figure 4.2, bottom).
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4.3 The Sensor Network 131
Fig. 4.2 (Top) Network topologies and connectivity in DSN, (Bottom) Summary of net-
work topology and technology classification
Star 1:N Topology Regular 2D P2P Mesh Topology
Mixed P2P Ad-Hoc
3D Mesh Topology
Irregular 2D P2P Mesh Topology
(a)
(b)
(c)
(d)
Topology
IrregularRegular
Technology
WirelessWired
electrical optical radio
Message
Passing
Switched
One-
dimensional
Two-
dimensional
N-
dimensional
Single
stage
Multi
stage
Crossbar
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Chapter 4. Distributed Sensor Networks
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There are hybrid approaches in wireless networks. Though macro-scale
sensor networks profit from wireless technologies due to reduced installation
and deployment complexity, micro-scale network embedded in material
structure can profit from wired technologies, as these are in general more
energy efficient than their wireless counterparts (directed energy flow in the
former in contrast to non- or semi-directed energy flow in the latter).
One of the simplest and still widely used topologies are master-slave 1:N
star networks. These are mainly used in centralized processing architectures.
There is only one connection from each client to a server node (connectivity
degree 1, distance 1). The failure of the server is critical. A different situation is
shown in the bottom row (b) using a broadcast medium (bus, Ethernet) which
connects N individual nodes, with a connectivity degree (N-1) and a maximal
distance of 1. Here a failure of the broadcast medium (the interconnection) is
critical.
Two-dimensional mesh-like networks, either fully or partially connected
and using point-to-point links, are the preferred topologies for wired planar
material-integrated sensor networks having the lowest interconnect complex-
ity but still providing robustness in the presence of missing or defective links
due to path redundancy (there is more than one possible path from a source
to a destination node). They have a connectivity degree of 4 and a maximal
distance of log
2
N. Furthermore, the logical network topology corresponds to
the geometrical placement order of nodes.
In fully connected networks (a) each node is connected with each other
node, leading to a connectivity degree of N-1 and a distance of 1 with the high-
est degree of robustness and lowest message passing latency, but requiring
the highest resource demands. Cube networks (three-dimensional topology,
middle row, right side) provide a good compromise between the afore men-
tioned networks, having a connectivity degree of 6 and a distance of log
3
N
(resulting in a lower message passing latency compared with two-dimensional
networks). They are a special case of a generic hypercube networks (n-dimen-
sional), with very limited practical use in material-integrated sensor network
due to the complex interconnect structure.
In chain or ring networks (c) the connectivity is 2, the maximal distance N-1,
providing no (chain) or low (ring) robustness. Here, failure of a single node can
already be critical: The chain is only as strong as its weakest link.
Hierarchical networks (d) provide node partitioning in spatial bounded sub-
networks connected with each other by using dedicated routers (for example,
sub-star networks arranged in chain networks applied in [GHE10]).
A chain network has no redundancy, in contrast to two- and three-dimen-
sional mesh and cube networks (with four and six neighbour node
connections, respectively). They provide an increasing number of alternative
paths without introducing additional high connectivity complexity and costs
(number of connections are in the order of nodes).
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4.3 The Sensor Network 133
Fig. 4.3 Abstraction of distributed computing in sensor networks with processes and
message passing
Distributed computing, i.e., in sensor networks, can be mapped on a com-
munication processes model as outlined in Figure 4.3. Communication
between processes is performed via network (or shared memory) message
passing queues.
4.3.2 Message-passing and Routing
The possible number of paths P connecting the (upper) left and (lower) right
nodes and the connectivity costs C (number of all links in a regular configura-
tion) of the network depend on the number of nodes (n,m,o) in each
dimension respectively and can be calculated from Equation 4.1 (assuming a
connecting path visits each node at most one time).
(4.1)
Figure 4.4 compares the redundancy of 1-, 2-, and 3-dimensional grid net-
works in relation to the number of nodes.
In macro-scale sensor networks nodes are often connected by using wire-
less technologies with dynamic ad-hoc network topologies. Protocol-based
routing in changing networks (regarding communication connections and
nodes) is one main challenge in the design of robust and energy-aware sensor
networks.
Pro Pro
Pro Pro
Communication
Net.
PE PE
PE PE
10101
Queue Queue Queue
Pn Pnm
nm
nm
Pnmo
nmo
nmo
Cn
12 3
1
1() (, )
()!
!!
(, ,)
()!
!!!
()
==
+
=
++
, ,
==− =
=−
n Cnm nmmn
Cnmo mnomno
12
3
2
3
, ( , ) ,
(, ,)
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Chapter 4. Distributed Sensor Networks
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Fig. 4.4 Redundancy in mesh-like networks (chain, grid, cube): possible paths from node
(0,..) to (n-1,..) depending on the network size (n nodes in each dimension)
Most traditional routing protocols rely on an address space uniquely identi-
fying nodes as well as routing tables storing network information usually
limited to a bounded spatial scope. Path finding is required for the process of
forwarding of a message, and is the main goal of routing by minimizing the
number of passed routers (e.g., nodes). Commonly there is a set of different
paths connecting nodes. In the past many routing algorithms were developed
(for wireless and wired networks) finding 1. Alternative paths in the case of
missing or non-operating connectivity in parts of the networks, and by 2. Find-
ing the shortest path (concerning the distance in hop counts and the delivery
latency). One examples is junction based routing (discussed in detail in
[BAD11]). Routing in large-scale wireless sensor networks is discussed in
detail in [LI11].
Geographic routing and addressing bases on geometrical relations
between nodes, for example, relative addressing specifying the relative dis-
tance between nodes, can be used to avoid absolute unique node addressing,
which is not applicable in large scale miniaturized networks with an initially
unknown configuration, like smart dust networks [WAR01].
The scaling of networking down to material-integrated level requires simpli-
fying and robust approaches. Some of them capable for microchip level
implementations satisfying low-resource constraints are summarized below.
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4.3 The Sensor Network 135
They all not rely on routing tables (saving memory and computation) as well
as they are able to adapt to network changes immediately.
4.3.3 Advanced -Routing with Backtracking
Basics
Traditional -distance routing can only be deployed in regular network
structures existing in mesh or grid like networks. Incomplete networks with
missing neighbour node connectivity and irregular networks with missing
nodes (disturbing the spatial regularity) require adaptive -distance routing
strategies and backtracking to overcome these irregularities, for example, iso-
lated traps, shown in Figure 4.1.
The reliable communication protocol SLIP [BOS12A] implements smart and
adaptive routing strategies suitable to be used in technical and material-inte-
grated peer-to-peer networks with a similarity of the logical and physical
network topology, that means, the neighbourhood connectivity of nodes can
be uniquely assigned to a specific geometric direction.
The protocol message format is scalable with respect to the network size
(address size class ASC, ranging from 4 to 16 bit), maximal data payload (data
size class DSC), ranging from 4 to 16 bit length) and the network topology
dimension size (address dimension class ADC, ranging from 1 to 4).
Network nodes are connected using (serial) point-to-point links, and they
are arranged along different metric axes of different geometrical dimensions:
a one-dimensional network (ADC=1) implements chains and rings, a two-
dimensional network (ADC=2) can implement mesh grids, a three-dimen-
sional (ADC=3) can implement cubes, and so on. Both incomplete (missing
links) and irregular networks (with missing nodes and links) are supported for
each dimension class, shown in Figure 4.1.
The main issue concerning message-based communication is routing and
thus addressing of nodes. Absolute and unique addressing of nodes in a high-
density sensor network is not suitable. An alternative routing strategy is delta-
distance routing, used by SLIP. A delta-distance vector specifies the way
from the source to a destination node counting the number of node hops in
each dimension.
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Chapter 4. Distributed Sensor Networks
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Fig. 4.5 Message based communication in two-dimensional networks using delta-dis-
tance vector routing. Networks with incomplete (missing links) and irregular
(missing nodes) topologies are supported by using smart routing rules.
SLIP Message Format
A message packet contains a header descriptor specifying the type of the
packet and the scalable parameters ASC, DSC, and ADC, shown in Table 4.1.
The network address dimension ADC reflects the network topology, the size
class ASC the upper size bounds of the network, and the data size class DSC
the upper bound of the data payload. There are two different main message
types: requests and replies.
A packet descriptor follows the header descriptor, containing: the actual
delta-vector , the original delta-vector
0
, a preferred routing direction , an
application layer port , a backward-propagation vector , and the length of
the following data part. The total bit length of the packet header depends on
the {ASC, DSC, ADC} scalable parameter tuple setting, which optimises appli-
cation specific the overhead and energy efficiency (spatially & temporarily).
Each time a packet is forwarded (routed) in some direction, the delta-vector is
decreased (magnitude) in the respective dimension entry. For example, rout-
ing in the x-direction results in:
1

1
1. A message has reached the
destination iff =(0,0,..) and can be delivered to the application port . There
are different smart routing rules, applied in the order showed below until the
packet can be routed (or discarded), shown in Algorithm 4.1. First the normal
XY routing is tried, where the packet is routed in each direction one after
NODE
1
NODE
2
NODE
3
NODE
4
NODE
5
NODE
6
NODE
9
NODE
10
NODE
11
NODE
12
Y
X
NODE
7
NODE
8
HDT PDT DATA
ADC
TYP
DSC
ASC
ΔΔ
1 Γω
π
LEN
N8 N9 N11
N10N7N6
N2 N5
N4N3
N1
Path
X
Y
D=(2,-2)
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4.3 The Sensor Network 137
another with the goal to minimize the delta count of each particular direction.
The part of the packet descriptor selects a preferred starting direction.
Default the x-direction is selected. If this routing strategy is impossible due to
missing connectivity, the next routing strategy is tested. The packet is tried to
send in the opposite direction, which will be marked in the gamma entry
part of the message packet descriptor. Opposite routing is used to escape
small area traps, thereby backward routing is used to escape large area traps
or to send the packet back to the source node indicating that the packet can-
not be delivered. The routing decision is based on the actual message entries
{, , } and achieves adaptive routing reflecting the actual network topology
and the path the message already had travelled, including back-end traps,
resulting in routing of alternative paths by choosing different routing
directions.
A message is only send to a neighbour node using the particular link iff the
connection to the neighbour node was negotiated and is fully operational
concerning the sending and the receiving of messages to and from the neigh-
Entry Size [bits] Description
HDT:ADC 2 Address Dimension Class
HDT:ASC 2 Address Size Class
HDT:DSC 2 Data Size Class
HDT:TYP 2 Message type = {Request,
Reply, Alive, Acknowledge}
PDT: Num(ADC)*Bits(ASC) Actual delta vector
PDT:
0
Num(ADC)*Bits(ASC) Original delta vector
PDT: 2*Num(ADC) Backward propagation vector
PDT: Bits(ADC) Preferred routing dimension
(x,y,..)
PDT: Bits(ASC) Application layer port
PDT:LEN Bits(DSC) Length of packet
DATA LEN*Bits(DSC) Data
Tab. 4.1 SLIP message format (HDT: header descriptor, PDT: packet descriptor,)
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Chapter 4. Distributed Sensor Networks
138
bour node. For this purpose, the communication controller sends periodically
ALIVE messages to all direct surrounding nodes and waits for ACKNOWLEDGE
messages send back from the neighbour node to check the state of a connec-
tion. Non-existing nodes can be detected this way, too.
The Routing Algorithm
Alg. 4.1 Functional specification of the adaptive smart routing algorithm in pseudo
notation (
in
: direction in which the message has arrived, moveto sends the
message in the given direction, finally applying the route function again, {}
:
ordered list starting with the preferred routing dimension
if contained,
raises a signal terminating a set iteration,
?: test for link to a neighbour node
in the given direction)
TypeM=Message(,
0
,,,,Len,Data)messagestrcuture
={NORTH,SOUTH,WEST,EAST,..) setofdirections
={SOUTH,WEST,..} setof’negative’directions
+
={NORTH,EAST,..} setof’positive’directions
DIM={1,2,..} setofdimensionnumbers
DEF=funiconvertcoordinatetotopologydirection
caseiof|‐1=>WEST|1=>EAST|‐2=>SOUTH|2=>NORTH|..
DEF=fundirconverttopology
tocoordinatedirection
casedirof|WEST=>‐1|EAST=>1|SOUTH=>‐2|NORTH=>2|..
DEF
route
norma
l=funM,
in
try
{iDIM|M.
i
0}

doiterateoveralldimensions
Gammaignoredforbackwardtravelandifthisdirectionisreached
ifM.
i
>0?((i))(0)M.
i
M.
i
0
)then
moveto((i))
route(Mwith
i
=M.
i
1,(i))
routed
ifM.
i
<0?((i))M.
i
=0then
moveto((i))
route(Mwith
i
=M.
i
+1,(i))
routed
done
false nosuccess
withroutedtrue success
DEF
route
opposite
=funM,
in
try
{iDIM}

do iterateoveralldimensions
ifM.
i
=0
in
(i)?((i))then
moveto((i))
route(Mwith[
i
=M.
i
+1,
i
=1],(i))
routed
ifM.
i
=0
in
(i)?((i))then
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4.3 The Sensor Network 139
moveto((i))
route(Mwith[
i
=M.
i
1,
i
=1],(i))
routed
done
false nosuccess
withroutedtrue success
DEF
route
backward
=funM,
in

try
{iDIM}

doiterateoveralldimensions
ifM.
i
=0
in
?((i))then
moveto((i));
route(Mwith
i
=M.
i
+1,
i
=1)
routed
ifM.
i
=0
in
+
?((i))then
moveto((i))
route(Mwith
i
=M.
i
1,
i
=1)
routed
ifM.
i
=‐1?((i))then
moveto((i))
route(Mwith
i
=M.
i
+1,(i))
routed
ifM.
i
=1?((i))then
moveto((i))
route(Mwith
i
=M.
i
1,(i))
routed
done
false nosuccess
withroutedtrue success
DEF
route=funM,
in
in
:incomingdirectionofmessageM
ifM.=(0,0,..)thendeliver(M.,M)
else
ifroute
normal
(M,
in
)then
ifroute
opposite
(M,
in
)then
ifroute
backward
(M,
in
)then
discard(M)
In a first routing attempt, the normal routing strategy is tried with the goal
to minimize the
-vector in each dimension of an ordered set of dimensions,
starting with the preferred routing dimension . If this is impossible due to a
lack of required connectivity the message is tried to send in one opposite
direction increasing the
-vector temporarily. Opposite travel is marked in the
entry of the message. Finally, if this strategy fails also, the message is sent
backward, finally reaching the source node again if there are no other routing
alternatives found on the back path. Some care must be taken to avoid send-
ing a message back to the direction from which it originally comes from
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Chapter 4. Distributed Sensor Networks
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(resulting in a ping-pong forwarding with a live lock). To summarize, this sim-
ple routing algorithm uses decision-making based on information stored in
the message, and neighbourhood connectivity retrieved by the current node.
The hardware implementation (using ConPro and standard cell ASIC synthe-
sis, details can be found in [BOS10A] and [BOS12A]) requires about 250k
equivalent gates, including 15k Flip-Flop (FF) registers, which relates to a chip
area of 2.5mm
2
assuming ASIC standard cell technology 0.18m. The design
is partitioned on ConPro programming level in 34 processes, communicating
by using 16 queues. There are parallel executed receiver and sender pro-
cesses for each communication port.
Robustness and Stability Analysis
A simulation of a sensor network consisting nodes arranged in a two-
dimensional matrix with 10 rows and 10 columns was performed by using a
multi-agent model. Messages and sensor nodes were modelled with agents. A
comparison of XY and smart routing using the routing rules introduced in
Algorithm 4.1 is shown in Figure 4.6. The diagram shows the analysis results of
operational paths depending on the number of link failures. A path is opera-
tional (reachable) iff a node (device under test), for example node at position
(2,2), can deliver a request message to a destination node at position (x, y)
with x2 y2, and a reply can be delivered back to the requesting node. A
failure of a specific link and node results in a broken connection between two
nodes. The right image in Figure 4.6 shows an incomplete network with 100
broken links.
Fig. 4.6 Robustness analysis with results obtained from simulation (left) and snapshot
of sensor network (right)
Request Agent
Reply Agent
Node Agent
Connection
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
4.3 The Sensor Network 141
Fig. 4.7 Stability analysis (live locked messages, left) and snapshot of sensor network
with trapped messages (right)
With traditional XY routing there is a strong decrease of operational paths,
from a specific node (DUT) to any other node, if the number of broken links
increases. Using smart routing increases the number of operational paths sig-
nificantly, especially for considerable damaged networks, up to 50%
compared with XY routing providing only 5% reachable paths any more.
Results from stability analysis shown in Figure 4.7 point out unstable behav-
iour under some particular network topologies. Though most situations are
live lock free, there are some live locked messages circulating for ever in some
isolated traps, shown for example in the snapshot on the right side of this
figure.
The previous introduced simple adaptive routing strategy with an optimized
scalable message header outperforms traditional table-based and diffusion-
based routing strategies in terms of resource and path-search efficiency
(details of common routing algorithms in DSN can be found in [LI11]). Live-
locks can be avoided and the visited node count can be improved by adding a
path-node list memory stored in the message, as proposed and evaluated in
[BEN13]. But the improvement in delivery probability, delivery latency, and
energy efficiency using a path history for the prediction and selection of alter-
native paths is compromised by a significantly increased size of the message
header, and additionally the header has a non-fixed size, which makes the
processing of messages more difficult.
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
Chapter 4. Distributed Sensor Networks
142
4.3.4 From Passive Messages to Active Agents
The previously adaptive routing of messages is used in various mobile MAS
presented in this book. The routing, originally part of the network node (oper-
ating) system is shifted towards agents and part of the agent behaviour, which
has an additional routing goal, making routing active by the agent itself. Exam-
ples can be found in Chapter 9. The nodes provide direct neighbourhood
connectivity only.
Active mobile agents used for communication and data distribution in sen-
sor networks has the advantages of adaptivity, ad-hoc behaviour, and forming
of self-organizing communication structures between agents. This includes,
e.g., the election of relay nodes in domains of networks and the propagation
along dynamic travelling paths between multiple data source and sink nodes,
shown in Fig 4.8.
Fig. 4.8 The comparison of traditional server-client communication (left) and mobile
agent model (right)
The mobile agent routing is a kind of complex combinatorial optimization
problem that solves the optimal path according to a sequence of visited
nodes and addressing energy efficiency in sensor networks, especially con-
cerning wireless sensor networks (WSN). Ant colony optimization (ACO) is put
forward to solve migration of mobile agent.
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S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
4.4 Further Reading 143
4.4 Further Reading
1. F. Hu and Q. Hao, Intelligent Sensor Networks, CRC Press, 2013, ISBN
9781420062212
2. M. J. McGrath and C. N. Scanaill, Sensor Technologies Healthcare, Well-
ness, and Environmental Applications, Apress Open, 2014, ISBN
9781430260134
3. R. Tynan, D. Marsh, D. O’Kane, and G. M. P. O’Hare, Intelligent agents for
wireless sensor networks, in Proceedings of the fourth international joint
conference on Autonomous agents and multiagent systems -
AAMAS’05, 2005, p. 1179.
4. A. Rogers, D. D. Corkill, and N. R. Jennings, Agent Technologies for Sensor
Networks, IEEE Intelligent Systems, vol. 24, no. 2, 2009.
5. W. Liu, Data Driven Intelligent Agent Networks for Adaptive Monitoring and
Control, Michigan Technological University, 2012.
6. S. S. Iyengar and R. R. Brooks, Distributed Sensor Networks. CRC Press,
2005.
7. Stefano Bonucelli, Agent-Based Routing Algorithms for a Wired Sensor
Network, Università degli Studi di Genova Scuola Politecnica, 2014.
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
Chapter 4. Distributed Sensor Networks
144
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)